GenArchBench: A genomics benchmark suite for arm HPC processors

Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 A ailable online 2 Ap il 2024 0167-739X/© 2024 The Au ho (s). Published by Else ie B.V. This is an open access a icle unde he CC BY-NC license (h p://c ea i ecommons.o g/licenses/by- nc/4.0/). Con en s lis s a ailable a ScienceDi ec Fu u e Gene a ion Compu e Sys ems jou nal homepage: www.else ie .com/loca e/ gcs GenA chBench: A genomics benchma k sui e o a m HPC p ocesso s Lo ién López-Villellasd,∗,1,Rubén Langa i a-Bení eza,Asa Badouha,Víc o So ia-Pa dosa, Quim Aguado-Puigb,Guillem López-Pa adísa,Max Doblasa,Ja ie Se oaine,Chulho Kim , Mako o Onog,Ad ià A mejacha,c,San iago Ma co-Solaa,c,Jesús Alas uey-Benedéd, Pablo Ibáñezd,Miquel Mo e óa,c aBa celona Supe compu ing Cen e , Ba celona, Spain bDepa men d’A qui ec u a de Compu ado s, Uni e si a Au ònoma de Ba celona, Ba celona, Spain cDepa men d’A qui ec u a de Compu ado s, Uni e si a Poli ècnica de Ca alunya, Ba celona, Spain dDepa amen o de In o má ica e Ingenie ía de Sis emas/A agón Ins i u e o Enginee ing Resea ch (I3A), Uni e sidad de Za agoza, Za agoza, Spain eA m Resea ch, Camb idge, Uni ed Kingdom Leno o Resea ch, Uni ed S a es gLeno o In as uc u e Solu ions G oup, Uni ed S a es ARTICLE INFO Keywo ds: Genomics A m High-pe o mance compu ing Pa allel compu ing Vec o compu ing Pe o mance cha ac e iza ion ABSTRACT A m usage has subs an ially g own in he High-Pe o mance Compu ing (HPC) communi y. Japanese supe - compu e Fugaku, powe ed by A m-based A64FX p ocesso s, held he op posi ion on he Top500 lis be ween June 2020 and June 2022, cu en ly si ing in he ou h posi ion. The ecen ly eleased 7 h gene a ion o Amazon EC2 ins ances o compu e-in ensi e wo kloads (C7 g) is also powe ed by A m G a i on3 p ocesso s. P ojec s like Eu opean Mon -Blanc and U.S. DOE/NNSA As a a e u he examples o A m i up ion in HPC. In pa allel, o e he las decade, he apid imp o emen o genomic sequencing echnologies and he exponen ial g ow h o sequencing da a has placed a signi ican bo leneck on he compu a ional side. While mos genomics applica ions ha e been ho oughly es ed and op imized o x86 sys ems, jus a ew a e p epa ed o pe o m e icien ly on A m machines. Mo eo e , hese applica ions do no exploi he newly in oduced Scalable Vec o Ex ensions (SVE). This pape p esen s GenA chBench, he i s genome analysis benchma k sui e a ge ing A m a chi ec u es. We ha e selec ed compu a ionally demanding ke nels om he mos widely used ools in genome da a analysis and po ed hem o A m-based A64FX and G a i on3 p ocesso s. O e all, he GenA ch benchma k sui e comp ises 13 mul i-co e ke nels om c i ical s ages o widely-used genome analysis pipelines, including base-calling, ead mapping, a ian calling, and genome assembly. Ou benchma k sui e includes di e en inpu da a se s pe ke nel (small and la ge), each wi h a co esponding eg ession es o e i y he co ec ness o each execu ion au oma ically. Mo eo e , he po ing ea u es he usage o he no el A m SVE ins uc ions, algo i hmic and code op imiza ions, and he exploi a ion o A m-op imized lib a ies. We p esen he op imiza ions implemen ed in each ke nel and a de ailed pe o mance e alua ion and compa ison o hei pe o mance on ou di e en HPC machines (i.e., A64FX, G a i on3, In el Xeon Skylake Pla inum, and AMD EPYC Rome). O e all, he expe imen al e alua ion shows ha G a i on3 ou pe o ms o he machines on a e age. Mo eo e , we obse ed ha he pe o mance o he A64FX is signi ican ly cons ained by i s small memo y hie a chy and la encies. Addi ionally, as p oo o concep , we s udy he pe o mance o a p oduc ion- eady ool ha exploi s wo o he po ed and op imized genomic ke nels. 1. In oduc ion Fo many yea s, A m p ocesso s ha e domina ed he mobile de ice segmen . Thei ene gy e iciency and license-based business model ha e been he pilla s unde pinning his success. ∗Co espondence o: Depa amen o de In o má ica e Ingenie ía de Sis emas/A agón Ins i u e o Enginee ing Resea ch (I3A), Uni e sidad de Za agoza, Spain. E-mail add ess: [email p o ec ed] (L. López-Villellas). 1The co esponding au ho conduc ed his wo k while a ilia ed wi h he Ba celona Supe compu ing Cen e . In ecen yea s, A m has bu s on o he high-pe o mance compu ing ma ke wi h in luen ial companies and conso iums ha ha e become licensees, such as Fuji su, Amazon, Apple, NVIDIA, Samsung, AMD, B oadcom, HUAWEI, and Qualcomm. Cu en ly, he A m-based Fuji su h ps://doi.o g/10.1016/j. u u e.2024.03.050 Recei ed 3 No embe 2023; Recei ed in e ised o m 25 Ma ch 2024; Accep ed 31 Ma ch 2024 Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 314 L. López-Villellas e al. A64FX p ocesso powe s he Japanese supe compu e Fugaku, which held he op posi ion on he Top500 lis be ween June 2020 and June 2022 and is cu en ly in he ou h posi ion. Mo eo e , Amazon has been using A m p ocesso s o powe i s cloud compu ing pla o m (AWS), s a ing in 2018 wi h he G a i on p ocesso . They ollowed wi h he second gene a ion o G a i on in 2019 and he ecen ly eleased G a i on3. In he nea u u e, NVIDIA G ace CPUs and Ampe e se e s will be leading u he e o s o b eak h ough A m in HPC. As a esul , la ge-scale compu ing in as uc u es, usually equipped wi h x86 and IBM Powe p ocesso s, now ha e an addi ional compe i i e al e na i e. Howe e , mos o he scien i ic code o HPC is no ully adap ed and op imized o A m a chi ec u es. O e he las decade, genome sequencing has become he co ne - s one o genomics and mode n p ecision medicine. Due o he apid imp o emen o sequencing echnologies, i is cu en ly possible o sequence an indi idual’s genome in less han 24 h. This b eak h ough has enabled e ec i e pe sonalized heal hca e, allowing he diagnosis and ea men o diseases based on each pe son’s unique genomic disposi ion [1]. Fu he mo e, genome sequencing has also been p o en c ucial in cance s udies [2], d ug de elopmen [3], o COVID-19 ou - b eak con ol [4]. In he pas 20 yea s, genome sequencing cos s ha e d opped d ama ically and he amoun o sequencing da a p oduced yea ly has inc eased exponen ially. Mo e no ably, his inc ease in da a p oduc ion has ou pe o med he pace o Moo e’s law. As a esul , a signi ican bo leneck in cu en genome sequencing analysis is placed on he compu a ional side, execu ing compu a ional-in ensi e genomics ools and pipelines. Genome analysis pipelines ha e his o ically been designed o un e icien ly on x86 a chi ec u es. Wi h he i up ion o A m-based HPC se e s, adap ing and op imizing genomics ools o exploi HPC A m a chi ec u es e ec i ely has become pa amoun . Fo ha , we ha e selec ed 13 compu a ionally-demanding CPU ke nels om he mos widely-used genomics ools, and we ha e included hem in a bench- ma k sui e called GenA chBench. All he ke nels exploi mul i-co e pa allelism and implemen common s ages om widely-used genome analysis pipelines such as base-calling, ead mapping, a ian call- ing, and de-no o assembly. Addi ionally, GenA chBench includes inpu da ase s o each ke nel (i.e., a small da ase and a la ge da ase pe ke nel) and hei co esponding ou pu s o be used as g ound u h. The small da ase s ha e been sized o equi e single- h ead execu ion imes no longe han a ew minu es ( o es ing pu poses); meanwhile, la ge da ase s equi e se e al minu es ( o pe o mance e alua ion pu poses). Fo con enience, we p o ide au oma ic eg ession es s o all he ke nels o e i y he co ec ness o he ou pu s. Fu he mo e, his wo k in oduces code adap a ions and op imiza- ions o he genomics ke nels a ge ing A m HPC CPUs. GenA chBench le e ages A m-speci ic HPC lib a ies (ca e ully op imized o A m p o- cesso s) and p esen s algo i hmic and code op imiza ions o exploi he a chi ec u e and esou ces o A m HPC machines. No ably, we ha e op imized some ke nels by u ilizing he la es A m Scalable Vec o Ex ensions (SVE) o le e age he po en ial o he la es A m HPC p ocesso s. In addi ion o he benchma k sui e po ing and op imiza ion, his wo k p esen s a pe o mance cha ac e iza ion o GenA chBench on ou HPC machines ( wo A m-based and wo x86-based nodes). The expe imen al e alua ion compa es he pe o mance o an A64FX p o- cesso , a G a i on3 p ocesso , an In el Xeon Skylake Pla inum 8160 p ocesso , and an AMD EPYC 7742 Rome p ocesso . This cha ac e i- za ion includes he ke nels’ ins uc ion b eakdown, single- h ead and mul i- h ead pe o mance e alua ions, a mic oa chi ec u e bo leneck analysis, and an ene gy- o-solu ion s udy in he di e en p ocesso s. Ul ima ely, we e alua e he pe o mance impac o hese op imiza ions by in eg a ing wo o he accele a ed ke nels in a p oduc ion- eady ool used in a my iad o genome analysis pipelines. In summa y, his wo k makes he ollowing con ibu ions: •We p esen GenA chBench, he i s benchma k sui e a ge ing A m HPC a chi ec u es o genome analysis pipelines and ools. The benchma k sui e is publicly a ailable a h ps://gi hub.com/ Lo ienLV/gena chbench/ eleases/ ag/1.0.0. •We p opose HPC adap a ions and code op imiza ions applied o GenA chBench’s ke nels o exploi he po en ial o A m HPC p o- cesso s, le e aging A m-speci ic HPC lib a ies and A m Scalable Vec o Ex ension (SVE). •We pe o m a comp ehensi e pe o mance cha ac e iza ion o GenA chBench in wo HPC A m p ocesso s (i.e., A64FX and G a i on3). We compa e he pe o mance o A m agains wo e e ence HPC x86 machines. 2. Backg ound Genome da a analysis pipelines comp ise mul iple s ages and com- pu a ional ools, om sequencing biological samples o de i ing mean- ing ul da a analysis esul s o scien is s and heal hca e p o essionals. This sec ion in oduces he main sequencing echnologies, pipelines, and ools used in common genome analysis (Fig. 1 shows a succinc g aphic summa y). 2.1. Sequencing echnologies Be o e any compu a ional analysis can be pe o med, biological DNA samples mus be con e ed o digi al da a. This p ocess is pe - o med by he sequencing machines (Fig. 1-1), and, despi e he ema k- able ad ances in he las decades, hese machines a e s ill unable o ead a comple e DNA molecule om end o end. Ins ead, sequencing machines allow eading ela i ely small chunks o DNA, called eads o agmen s, om andom loca ions wi hin he dono ’s DNA genome. A e wa ds, sequenced eads mus be jigsaw oge he o econs uc o eassemble he o iginal dono ’s genome. Sequencing machines a e commonly ca ego ized in o h ee gene a- ions based on hei echnological ad ancemen s. The i s sequencing echnologies (Sange e al. [5] and Maxam e al. [6]) we e de eloped in 1977 and used o sequence he i s d a o he human genome in 2000 [7]. Since hen, sequencing echnologies ha e e ol ed quickly, simpli ying he sequencing p ocess and inc easing he da a-p oduc ion h oughpu . In he mid-2000s, second-gene a ion echnologies [8] we e in oduced and soon eplaced i s -gene a ion echnologies. Second- gene a ion echnology can gene a e ixed-leng h sequences o 100–300 bps a a h oughpu o ens o gigaby es pe hou and wi h a low eading e o a e (0.1% o he ead leng h). A p esen , Illumina domina es he ma ke o second-gene a ion sequencing machines. Recen ly in oduced hi d-gene a ion echnologies, known as long- ead sequencing, can ead a iable-leng h sequences o conside able leng h (i.e., ens o kilo base- pai s) a he expense o lowe p oduc ion h oughpu (less han 10 Gb/hou ) and highe eading e o a e (0.1%–10% o he ead leng h). Paci ic Biosciences (PacBio) and Ox o d Nanopo e Technologies (ONT) a e he mos no able manu ac u e s o hi d-gene a ion sequencing echnologies. 2.2. Genome da a analysis pipelines and ools Be o e any p ocessing can be pe o med, he sequencing machines’ aw signals mus be ans o med in o sequences o nucleo ides (A, C, G, T). This p ocess is called basecalling (Fig. 1-2). Typically, a specialized basecalling ool is used o pe o m his p ocess ailo ed o each sequencing echnology. Fo ins ance, Boni o [9] and Guppy [10] a e wo o he mos widely-used ools o basecalling Ox o d Nanopo e’s aw-signal ou pu . Once he sequences o nucleo ides a e decoded, sequenced eads mus be p ocessed and analyzed o de i e meaning ul biological in- sigh s. Al hough many di e en genome analyses can be pe o med using sequenced da a, mos analyses begin wi h ei he genome ese- quencing (1-3.a) o genome assembly (1-3.b). Bo h analyses seek o econs uc he sample’s genome by pu ing oge he all he sequenced eads. Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 315 L. López-Villellas e al. Fig. 1. Wo k low diag am o common genome analysis pipelines. Going om (1) sequencing, h ough (2) basecalling, o (3.a) genome esequencing o (3.b) and genome assembly. The igu e shows he di e en compu a ional ke nels used wi hin each s age o ool. 2.2.1. Genome esequencing The mos common app oach o econs uc ing he sample’s genome is by esequencing and in ol es econs uc ing he sample’s genome using a p e iously known e e ence genome. Fo ha , each sequenced ead is loca ed and ma ched o he mos likely o igina ing posi ion in he e e ence genome, allowing small di e ences (e.g., misma ches, inse ions, and dele ions). This p ocesses is called ead mapping (Fig. 1- 3.a.1) and i is implemen ed by many ools like BWA-MEM2 [11,12], Minimap2 [13], Bow ie2 [14,15], and GEM [16]. Read mapping is one o he mos compu a ionally expensi e s eps in all genome sequence analyses. Consequen ly, ead mapping has been ex ensi ely s udied and op imized. Mos sequence mappe s a e based on he seed-chain-ex end ech- nique. This echnique implemen s h ee algo i hmic s eps o swi ly loca e and align a sequence wi h a e e ence genome. Du ing he i s s ep, known as seeding (Fig. 1-3.a.1.1), he mappe sea ches small subsequences o he eads (seeds) in he e e ence le e aging an index s uc u e. The mos widely-used indexes used o seeding a e FM- Index [17] and hash- ables [18,19]. Seeding educes he po en ial num- be o loca ions in he e e ence whe e a sequence can ma ch, dec eas- ing he amoun o wo k pe o med in subsequen s eps. A e wa ds, a chaining s ep (Fig. 1-3.a.1.2) is pe o med o educe u he he lis o possible ma ching loca ions in he e e ence. Du ing he chaining s ep, all he mapped seeds a e p ocessed o ind a colinea chain o seeds ha can po en ially ma ch he inpu sequence. Finally, du ing he ex- ensión o alignmen s ep (Fig. 1-3.a.1.3), he inpu sequence is aligned agains he candida e loca ion in he e e ence genome, disco e ing he di e ences be ween he dono ’s sequence and he e e ence genome. Usually, a dynamic p og amming-based algo i hm, such as Needleman– Wunsch [20] o Smi h–Wa e man–Go oh [21,22], is used o compu e he alignmen . A e sequence mapping, once he eads a e loca ed in he e e - ence genome, a a ian calling algo i hm (Fig. 1-3.a.2) de e mines he a ian s and mu a ions be ween he dono ’s genome and he e e - ence genome. These a ia ions p o ide c ucial insigh s in o he ge- ne ic makeup o he sequenced indi idual, po en ially e ealing ge- ne ic a ia ions ha may be associa ed wi h diseases and heal h con- di ions. No able examples o widely-used a ian calle s a e GATK Haplo ype-Calle [23], Pla ypus [24], Clai [25,26], DeepVa ian [27] and Medaka [28]. 2.2.2. Genome assembly Despi e he simplici y and e ec i eness o genome esequencing, he e is s ill a lack o high-quali y e e ence genomes o many species. In hose si ua ions, genome de-no o assembly (Fig. 1-3.b) is used o econs uc he dono ’s genome om sc a ch jigsawing he sequenced eads oge he . Mos popula de-no o assembly me hods ely on de B uijn g aphs. Fo a gi en se o sequences, i s co esponding de B uijn g aph con ains a node pe each sequence’s k-me (i.e., sub-s ing o leng h 𝑘nu- cleo ides) and an edge ha connec s adjacen and o e lapping k-me s. Be o e cons uc ing he de B uijn g aph o a se o inpu sequences, he numbe o unique k-me s in he eads is coun ed (Fig. 1-3.b.1) o p une he leas equen ones (likely a i ac s o he sequencing p ocess). A e wa ds, he de B uijn g aph is cons uc ed (Fig. 1-3.b.2). Then, he consensus sequence is de i ed using mul iple sequence alignmen (MSA) algo i hms (Fig. 1-3) and he cons uc ed de B uijn g aph. No able examples o de B uijn g aph based assemble s a e Flye [29], Canu [30], and Racon [31]. 2.2.3. Me agenomics Beyond genome esequencing, a ian calling, and de-no o assem- bly, many p e iously desc ibed analysis s eps and ools can be ound in o he genome analysis pipelines. This is he case o many me age- nomics analysis pipelines. Me agenomics pipelines seek o analyze genomic in o ma ion om mixed mic obial communi ies, p o iding insigh s in o he di e si y, in e ac ions and unc ion o mic oo gan- isms p esen in an en i onmen al sample. Me agenomics analyses a e pe o med using ools such as Cen i uge [32], RawMap [33], UN- CALLED [34], ReadFish [35], K aken2 [36] and Cla k [37]. These ools employ k-me coun ing (Fig. 1-3.b.1) and seeding echniques (Fig. 1-3.a.1.1) o hei analysis. Mo eo e , a ian calle s like GATK Haplo ype Calle [23] and Pla ypus [24] a e used o cons uc De B uijn g aphs (Fig. 1-3.b.2) and co ec a i ac s p oduced du ing he map- ping p ocess (Fig. 1-3.a.1). Fu he mo e, he chaining p ocess (Fig. 1- 3.a.1.2) is also u ilized o genome assembly when using al e na i e app oaches based on de B uijn g aphs [38]. 3. GenA ch benchma k sui e The GenA ch benchma k sui e comp ises 13 mul i h eaded CPU ke nels de i ed om he mos widely used genomics ools and co e s Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 316 L. López-Villellas e al. he mos impo an genome sequencing s eps. I includes en ke nels om he GenomicsBench [39] benchma k sui e and h ee addi ional ke nels: he Bi -Pa allel Mye s algo i hm [40] (BPM), he Wa e on Alignmen algo i hm [41] (WFA), and FAST-CHAIN [42]. BPM and WFA complemen he sequence alignmen ke nels o GenomicsBench o be e cap u e con empo a y ends. Addi ionally, FAST-CHAIN [42] is a ecen ec o -enabled eimplemen a ion o he CHAIN ke nel p esen in GenomicsBench, which allows us o u he explo e he capabili ies o SVE. Addi ionally, GenA chBench includes inpu da ase s o each ke - nel (i.e., a small da ase and a la ge da ase pe ke nel) and hei co esponding ou pu s o be used as g ound u h. The small da ase s ha e been sized o equi e single- h ead execu ion imes no longe han a ew minu es ( o es ing pu poses); meanwhile, la ge da ase s equi e se e al minu es ( o pe o mance e alua ion pu poses). Fo con enience, we p o ide au oma ic eg ession es s o all he ke nels o e i y he co ec ness o he ou pu s. Al hough some ke nels included in GenA chBench can exploi he capabili ies o mode n GPUs, his esea ch ocuses on po ing, accel- e a ing, and e alua ing he pe o mance o genomics ke nels in A m p ocesso s. Mo eo e , he A m-sys ems e alua ed in his wo k (A64FX and G a i on3) a e no equipped wi h GPUs. The ollowing ex p esen s GenA chBench’s ke nels, b ie ly desc ib- ing i s unc ionali y, which ools use hem, and a desc ip ion o hei usage and inpu s. Adap i e Banded Signal o E en Alignmen (ABEA): ABEA is a dynamic p og amming algo i hm ha compa es aw nanopo e sig- nals om ONT sequencing machines o a e e ence genome sequence. ABEA’s implemen a ion is based on he Suzuki–Kasaha a (SK) [43] al- go i hm. This s ep is pe o med in some ools, such as Nanopolish [44], o co ec e o s p oduced in he basecalling p ocess (Fig. 1-2). Fo GenA chBench, we ha e used he CPU implemen a ion o 5c [45], a e sion o ABEA based on Nanopolish’s, op imized o bo h CPU- only and hyb id CPU/GPU execu ions. This implemen a ion o ABEA exploi s coa se-g ain mul i- h eading by di iding he aw signals o he inpu be ween he a ailable co es. Since he signals a e no o egula size, 5c implemen s wo k-s ealing o imp o e load balance. The small and la ge inpu s comp ise 1K and 10K aw FAST5 (ONT) eads om ch omosome 22 o NA12878 and GRCh38 as he e e ence genome [46]. Bi -Pa allel Mye s (BPM): BPM [40] is a dynamic p og amming algo i hm ha inds all loca ions a que y s ing o size 𝑚ma ches a e e ence s ing o size 𝑛wi h 𝑘o ewe di e ences (Fig. 1-3.a.1.3). I compu es he app oxima e s ing ma ching o wo s ings in 𝑂(𝑚𝑛∕𝑤) ime, whe e 𝑤is he wo d size o he machine. BPM is used in ead map- ping ools, such as GEM-Mappe [16], Edlib [47], G aphAligne [48] o Hobbes [49]. Fo GenA chBench, we ha e used an in-house imple- men a ion o he algo i hm ha exploi s mul i- h eading by assigning di e en pai s o s ings o di e en h eads. The small and la ge inpu s comp ise 100K and 10M sequence pai s om human sample SRR7733443 downloaded om he sequence ead a chi e [50]. Banded Smi h–Wa e man (BSW): The Smi h–Wa e man algo i hm [21] is a dynamic p og amming algo i hm ha compu es he local sequence alignmen o wo sequences o leng h 𝑚and 𝑛, espec i ely, in 𝑂(𝑚𝑛) ime and space. A banded e sion o Smi h–Wa e man [51] is used o align sequences wi h a maximum o 𝑤inse ions/dele ions, educing he ime and space complexi y o 𝑂(𝑤𝑛)(Fig. 1-3.a.1.3). BSW is used in a ian disco e y ools such as GATK [23], and in sequence alignmen so wa e like BWA-MEM [11,12]. Fo GenA ch- Bench, we ha e used BWA-MEM2’s x86- ec o ized implemen a ion o BSW. In o de o exploi mul i- h eading, he se o pai s o s ings o align is dynamically di ided be ween p ocesso s. The small and la ge inpu s comp ise 100K and 10M sequence pai s om human sample SRR7733443 [50]. Seed Chaining (CHAIN): Gi en he se o seeds om a DNA se- quence ( ead) mapped o ano he sequence, such as he e e ence genome, he chaining s ep (Fig. 1-3.a.1.2) aims o ind a chain o colinea seeds. This is a ime-consuming s ep pe o med by alignmen ools, such as Minimap2, and by de-no o assemble s like Flye [29] o Canu [30]. We ha e used he implemen a ion o CHAIN ound in GenomicsBench ha ex ends Minimap2’s o exploi in e - ask pa al- lelism ac oss eads. The small and la ge inpu s comp ise he seeds om 1K, and 10K eads o Pacbio’s Caeno habdi is elegans wo m sequence da a [52]. SIMD Seed Chaining (FAST-CHAIN): The p e iously p esen ed implemen a ion o he CHAIN algo i hm u ilizes heu is ics o s op execu ing when he esul is su icien ly good. This speedups execu ion a he cos o accu acy, and i hinde s he ec o iza ion o he ke nel. FAST-CHAIN [42] is an x86- ec o ized e sion o CHAIN ha emo es he heu is ics o exploi SIMD compu a ion. As a esul , FAST-CHAIN ou pu s accu a e esul s and p esen s pe o mance gains compa ed o CHAIN. FAST-CHAIN uses he same inpu s as CHAIN. De B uijn G aph Cons uc ion (DBG): The De B uijn g aph (DBG) o an inpu se o eads is used o ep esen he o e laps be ween he sub-s ings o leng h 𝑘(k-me s) ound in he inpu (Fig. 1-3.b.2). Each node o he g aph ep esen s a k-me and he edges connec adjacen k-me s in he inpu se . The cons uc ion o hese g aphs is a ime- consuming s ep in de-no o assemble s like Flye [29], Canu [30] o Racon [31], and in a ian calle s such as GATK [23] and Pla ypus [24]. Fo GenA chBench, we ha e used he DBG cons uc ion o Pla ypus, which exploi s pa allelism by assigning di e en egions o he inpu o di e en h eads. Bo h inpu s employ ch omosome 22 o BWA-MEM aligned eco ds om he Pla inum Genomes da ase [53]. The small inpu uses bases 16M-16.5M, while he la ge inpu uses he en i e ch omosome. FM-Index Sea ch (FMI): The FM-index is a comp essed sub-s ing index based on he Bu ows–Wheele ans o m [54]. Gi en a sub-s ing 𝑠, FM-index can be used o ind he loca ion o 𝑠in he e e ence genome in 𝑂(|𝑠|) ime, whe e |𝑠|is he leng h o he sub-s ing (Fig. 1- 3.a.1.1). The FM-index da a s uc u e is used in sequence alignmen ools such as BWA-MEM [11,12] o Bow ie2 [15], and in me agenomic classi ica ion so wa e like Cen i uge [32]. Fo GenA chBench, we ha e used he supe -maximal exac ma ch ke nel o BWA-MEM2, which u ilizes he FM-Index s uc u e. This ke nel exploi s pa allelism by dynamically assigning ba ches o eads among h eads. The small and la ge inpu s comp ise 1M and 10M pai s o 151 bases om human sample SRR7733443 [50]. K-me Coun ing (KMER-CNT): K-me coun ing aims o coun he numbe o occu ences o each k-me in an inpu sequence (Fig. 1- 3.b.1). This ask is pe o med in de-no o assemble s such as Flye [29] o Canu [30] and in me agenomics classi ica ion so wa e like Cla k [37]. Addi ionally, no e ha he unc ionali y o KMER-CNT is e y simila o accessing la ge lookup ables, as done in s a e-o - he-a map- pe s like Minimap2 [13]. Fo GenA chBench, we ha e used he k-me coun ing ke nel o Flye. This implemen a ion di ides he inpu - eads be ween h eads and elies on he h ead-sa e hash-map implemen a- ion o Libcuckoo lib a y [55] o concu en ly inc ease he numbe o indi idual k-me s shown by each h ead. The small and la ge inpu s comp ise 1K and 50K Esche ichia coli Ox o d Nanopo e eads sequenced by Loman Labs [56]. Neu al Ne wo k-based Base Calling (NN-BASE): ONT sequencing machines moni o changes in an elec ical cu en as single s ands o DNA o RNA pass h ough a p o ein nanopo e. These changes in he elec ical cu en a e hen con e ed o a sequence o nucleo ide bases in he basecalling p ocess (Fig. 1-2). The analog signal ine i ably con- ains ambigui ies due o noise o measu emen e o s. Some basecalle s, such as Guppy [10] and Boni o [9], ely on neu al ne wo ks o sol e hese ambigui ies, de e mining he mos likely obse ed nucleo ide in each pa o he elec ical cu en . Fo GenA chBench, we ha e used Boni o’s deep-lea ning base-calle (NN-BASE), which depends on he PyTo ch lib a y [57]. Boni o spli s he inpu signal in o smalle chunks o egula size and eeds hem o a PyTo ch neu al ne wo k ha Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 317 L. López-Villellas e al. Table 1 Cha ac e is ics o e iew o he expe imen al se up. A64FX G a i on3 SKX Rome Co es 4 ×12 (+ 4 assis an ) 64 2 ×24 64 SMT No No Disabled Disabled F equency 2.2 GHz (s a ic) 2.6 GHz 1–2.1 GHz (dynamic) 1.5–2.25 GHz (dynamic) Max. powe 120 W N/A 2 ×150 W 225 W Mem. capaci y 4 ×8 GB 8 ×16 GB 2 ×6×8 GB 16 ×64 GB Mem. echnology on-package HBM2 o -package DDR5 4800 MHz o -package DDR4 2667 MHz o -package DDR4 3200 MHz Peak bandwid h 4 ×256 GB/s 300 GB/s 2 ×120 GB/s 204.8 GB/s L1i 64 KB (4-way) 64 KB 32 KB (8-way) 32 KB (8-way) L1d 64 KB (4-way) 64 KB 32 KB (8-way) 32 KB (8-way) L2 – 1 MB 1 MB (16-way) 512 KB (8-way) LLC 4 ×8 MB (16-way) 32 MB 2 ×33 MB (11-way) 16 ×16 MB (16-way) Vec o ex ension NEON/SVE 512 bi s NEON/SVE 256 bi s SSE/AVX2/AVX512 SSE/AVX2 in e nally exploi s mul i- h eading. The small and la ge inpu s comp ise 1 and 10 aw FAST5 eads om ch omosome 20 o NA12878, ob ained om he Nanopo e WGS Conso ium [46]. Neu al Ne wo k-based Va ian Calling (NN-VARIANT): Va ian calling is he p ocess o de ec ing he di e ences ( a ian s o mu a ions) be ween he aligned eads and he e e ence genome (Fig. 1-3.a.2). This is a cos ly p ocess pe o med by s a is ics-based a ian calle s, such as GATK Haplo ypeCalle [23] o Pla ypus [24], and deep-lea ning a ian calle s, such as Clai [25,26], DeepVa ian [27] o Medaka [28]. Fo GenA chBench, we ha e used he second gene a ion o Clai a ian calle (Clai 3), based on he Tenso Flow amewo k [58]. Clai 3 ex- ploi s pa allelism by di iding he inpu in o egula -size chunks, and each o hese chunks is p ocessed by one h ead using Tenso Flow. Ou small and la ge inpu s comp ise 100K and 10M e e ence posi ions, espec i ely, o ch omosome 20 o HG002 om NITS’s Genome in a Bo le (GIAB) p ojec [59]. We a e using Clai 3’s ONT p e- ained model 941_p om_hac_g360+g422 [60]. Pileup Coun ing (PILEUP): Gi en he alignmen da a o a se o aligned eads o a egion o a e e ence genome, usually a SAM o BAM ile [61], pileup coun ing is he p ocess o summa izing he base-pai in- o ma ion a each ch omosomal posi ion. This summa y, called pileup, is cus oma y he inpu o long- ead neu al ne wo k a ian calle s such as Clai [25,26] o Meda aka [28] (Fig. 1-3.a.2). Fo GenA chBench we ha e used he pileup coun ing implemen a ion o Medaka, which exploi s mul i- h ead pa allelism by dis ibu ing 100 kilobase egions o he e e ence genome be ween h eads. The small inpu comp ises bases 1-1499707 o he S aphylococcus au eus genome [10], and he la ge inpu comp ises bases 1-1412827 o ch omosome 20 o sample HG002 [59]. Pa ial-O de Alignmen (POA): The cons uc ion o an o e lap g aph om a se o eads leads o an app oxima e ep esen a ion o he o iginal sample’s genome. To de e mine he consensus genome o he sample, he alignmen o all he eads agains each o he is pe o med in a p ocess called mul iple sequence alignmen (MSA) (Fig. 1-3.b.3). The Pa ial O de ed Alignmen (POA) algo i hm [62] compu es he MSA o all sequences by inc emen ally cons uc ing a pa ially-o de g aph aligning new sequences o i using a dynamic p og amming algo i hm such as Smi h–Wa e man [21] o Needleman–Wunsch [20]. The mul iple alignmen sequence (consensus sequence) is in e ed om he g aph by using he Hea ies Bundle algo i hm [63]. POA is used in so wa e packages such as Nanopolish [44] o Racon [31]. Fo Gena chBench we ha e used he SIMD-op imized e sion o POA o he SPOA lib a y [64]. SPOA exploi s mul i- h eading by compu ing he pa ially-o de ed g aph o mul iple se s o sequences in pa allel. The small and la ge inpu s comp ise 1K and 6K se s o mul iple sequences aligned o a e e ence genome, each con aining be ween 5 and 115 sequences. This da a comes om Minimap2’s polishing s ep o he Flye-assembled S aphylococcus Au eus genome [10]. Wa e on Alignmen (WFA): The wa e on alignmen algo i hm (WFA) [41] is a pai wise alignmen algo i hm (Fig. 1-3.a.1.3) ha akes ad an age o homologous egions be ween he sequences o accele a e he alignmen p ocess. As opposed o adi ional dynamic p og amming algo i hms ha un in quad a ic ime, WFA ime complexi y is 𝑂(𝑛𝑠), p opo ional o he ead leng h 𝑛and he alignmen sco e 𝑠, using 𝑂(𝑠2)memo y. The wa e on algo i hm is used in ools such as w - mash [65], Ancho Wa e [66] o Ances alClus [67]. GenA chBench uses a cus om mul i- h ead implemen a ion o he algo i hm, in which each h ead wo ks in he alignmen o a pai o s ings. The small and la ge inpu s comp ise 100K and 1M sequence pai s om human sample SRR7733443 [50]. 4. Expe imen al se up Ou expe imen al se up consis s o wo A m and wo x86 HPC sys ems: a compu e node ea u ing an A m-A64FX p ocesso (A64FX), a c7 g.16xla ge Amazon-EC2 ins ance (G a i on3), a sys em wi h wo x86-64 In el Xeon Skylake Pla inum 8160 (SKX), and a compu e node wi h one x86-64 AMD EPYC 7742 Rome p ocesso (Rome). Table 1 p esen s an o e iew o he main cha ac e is ics o he ou sys ems. In e ms o compu ing co es, he A64FX is based on ou Non- Uni o m Memo y Access (NUMA) domains wi hin he chip, also e- e ed o as co e memo y g oups (CMG). Each NUMA domain has 12 co es, plus one assis ance co e no used o gene al compu ing ( unning daemons, I/O, asynch onous MPI, e c.). In o al, he A64FX implemen s 48 compu ing co es. G a i on3 implemen s 64 co es in a single NUMA domain. The AMD Rome CPU comp ises 8 co e chiple s, known as co e cache dies (CCD), and a cen al I/O die ha con ols all he I/O and memo y unc ions o he chip. A CCD has wo co e complex (CCX) clus e s, each wi h 4 co es. Any pai o CCDs can communica e h ough he I/O die. SKX con ains wo NUMA chips, each wi h 24 physical co es. Rega ding ope a ional equency, G a i on3 p esen s he highes maximum equency among he sys ems wi h 2.6 GHz. The o he h ee sys ems’ maximum equency is e y simila , anging be ween 2.1 and 2.25 GHz. Bo h x86 sys ems dynamically adjus hei equency based on hei load. Addi ionally, SKX educes i s equency when execu ing AVX/AVX512 ins uc ions. In con as , he A64FX ope a es a a ixed equency se o 2.2 GHz. The e is no public in o ma ion abou adap i e equency ope a ion on G a i on3. Wi h espec o SIMD ex ensions, he A64FX is he i s CPU o implemen he A m 8.2-A Scalable Vec o Ex ension (SVE) [68]. One o SVE’s main ea u es is ha i is Vec o Leng h Agnos ic (VLA); ha is, he same bina y wo ks on a chi ec u es implemen ing ec o egis e s o di e en leng hs anging om 128 o 2048 bi s. The A64FX implemen s 512 bi s SVE egis e s. G a i on3 also implemen s SVE, wi h a ec o leng h o 256 bi s. The A64FX and G a i on3 also suppo he A m Neon SIMD ex ension, a non-VLA SIMD ISA ha wo ks wi h 128-bi ec o s. Bo h x86 sys ems implemen he SSE and AVX2 SIMD ex ensions, wi h a ec o leng h o 128 and 256 bi s, espec i ely. The SKX also suppo s he AVX512 ex ension, wi h a ec o leng h o 512 bi s. None o he x86 SIMD ex ensions a e VLA. Conce ning main memo y, each A64FX’s NUMA domain has i s own local on-chip 8 GB HBM2 main memo y and can access he o he h ee NUMA domains’ local memo ies ia a ing bus. G a i on3 is connec ed o 8 ×16 GB DDR5 channels, o a o al o 128 GB o memo y. Each Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 318 L. López-Villellas e al. Table 2 Load- o-use memo y la encies in nanoseconds o he expe imen al se up. A64FX G a i on3 SKX Rome L1 2.3–5 1.5 1.9 1.8 L2 – 4.6 6.7 3.5 LLC 16.8–21.4 33.1 25.1 13.0 Main Mem. Local 118.2–126.4 153.5 86.2 121.5 Main Mem. Remo e 187.7–242.3 – 144.0 – chip o he SKX is connec ed o 6 ×8 GB DDR4 local channels and can access he o he chip’s local memo y. The Rome CPU is connec ed o 16 ×64 GB DDR4 channels, o aling 1 TB o memo y. The cache hie a chy o ganiza ion o he p ocesso s is ela i ely di e en . Bo h A m machines ha e wo 64 KB p i a e L1 caches pe co e (ins uc ions and da a), while he x86 CPUs ea u e wo 32 KB p i a e L1s pe co e. G a i on3 and SKX include one p i a e 1MB L2 cache pe co e, and Rome has one 512 KB p i a e L2 pe co e. The A64FX has one 8 MB las -le el cache (LLC) pe NUMA domain, G a i on3 includes one 32 MB LLC, SKX has wo 33 MB LLCs (one pe NUMA domain), and Rome includes one 16 MB LLC pe each 4-co e CCX. Conce ning memo y bandwid h, he A64FX is designed o achie e good pe o mance execu ing high memo y bandwid h-demanding ap- plica ions. The peak bandwid h o his chip (4 ×256 GB/s) is nea ly 3.5 imes highe han he peak bandwid h o G a i on3 (300 GB/s), he second sys em among he s udied in e ms o memo y h oughpu . I is ollowed by SKX, eaching up o 120 GB/s pe chip (240 GB/s in o al), and Rome holds he las posi ion wi h a peak bandwid h o 204.8 GB/s. Table 2 p esen s he memo y access la encies o each le el o he memo y hie a chy o all machines. All la encies on Rome and G a i on3 and la encies o emo e memo ies on he A64FX ha e been measu ed using he LMbench benchma k [69]. La encies o cache and local memo y on he A64FX ha e been ex ac ed om he mic o- a chi ec u e manual o he CPU. La encies on SKX ha e been measu ed using In el Memo y La ency Checke . The numbe o cycles o access he A64FX caches depends on he ype o ins uc ion: scala , loa ing- poin , sho SIMD, and la ge SIMD. The la encies o access he L1 on he sys ems ange om 1.5 ns (G a i on3) o 5 ns (la ge SIMD access on he A64FX). E en hough scala accesses on he A64FX a e as e (2.3 ns), i s ill p esen s he highes L1 access la ency. As p esen ed p e iously, he A64FX only implemen s wo le els o caches (L1 and LLC). The L2 access la encies o he o he sys ems ange be ween 3.5 ns (Rome) o 6.7 ns (SKX). Rome p esen s he as es access o i s LLC (13 ns), closely ollowed by he A64FX (16.8 ns o scala access and 21.4 ns o la ge SIMD access). The LLC access la ency on he SKX and G a i on3 is 25.1 and 33.1 ns, espec i ely. SKX p esen s he as es access la ency o local main memo y (86.2 ns), ollowed by he A64FX and Rome, wi h simila la encies (∼120 ns). G a i on3 has he highes local memo y access la ency, as expec ed om cu en DDR5 SDRAMs. Accessing emo e main memo ies in he A64FX akes be ween 187.7 ns (nea - emo e memo y) and 242.3 ns ( a - emo e memo y). Accessing he o he chip’s main memo y on he SKX machine akes 144 ns, 23% as e han A64FX’s bes case. The ou -o -o de esou ces o he expe imen al se up a e p esen ed in Table 3. We assume ha G a i on3 implemen s he same esou ces as Neo e se V1 o non-publicly a ailable da a (ma ked wi h *). Una ail- able da a o nei he G a i on3 no Neo e se V1 is ep esen ed as N/A. The A64FX is igh in ou -o -o de esou ces compa ed wi h he o he h ee p ocesso s. The SKX and Rome ha e a simila numbe o physical egis e s, almos doubling he numbe o gene al-pu pose egis e s o he A64FX (180 s. 96) and implemen ing 30% mo e SIMD/FP egis e s han he A64FX (160 s. 128). The A64FX can issue up o 7 mic o- ope a ions (𝜇OP) pe cycle, G a i on3 can issue up o 15, 8 o SKX, and 11 o Rome. The A64FX and SKX a e capable o commi ing 4 mic o- ope a ions pe cycle. Howe e , SKX can me ge wo mic o-ope a ions Table 3 Ou -o -o de esou ces o he expe imen al se up. A64FX G a i on3 SKX Rome Gene al egis e s 96 N/A 180 180 SIMD/FP egis e s 128 N/A 168 160 Issue wid h 7 (𝜇OP) 15 (𝜇OP) 8 (𝜇OP) 11 (𝜇OP) Commi wid h 4 (𝜇OP) N/A 4-8 (𝜇OP) 8 (MOP) ROB (en ies) 128 256* 224 224 LB (en ies) 40 85* 72 44 SB (en ies) 24 90* 56 48 RS (en ies) 2 ×20 + 2×10 + 19 N/A 97 4 ×16 + 28 + 36 *Neo e se V1 CPU de aul s. in o one used mic o-ope a ion, inc easing i s heo e ical commi a e o 8 mic o-ope a ions. Rome can commi up o 8 mac o-ope a ions (MOP) – i.e., ALU, memo y, o me ged ALU/memo y ope a ion – pe cycle. The eo de bu e (ROB) o G a i on3 (256 en ies) is wice as big as he A64FX’s (128 en ies). SKX and Rome ha e an iden ical-size ROB (224 en ies). The sizes o he load bu e s (LB) and s o e bu e s (SB) o he CPUs a e ela i ely di e en . G a i on3 and SKX implemen he la ges LB, wi h 85 and 72 en ies, espec i ely. The LB o he A64FX has 40 en ies, and Rome implemen s a 44-en y LB. Simila ly, G a i on3 and SKX ha e he la ges SB (90 and 56 en ies, espec i ely). The A64FX implemen s a 24-en y SB, hal he size o Rome’s. Addi ion- ally, a s o e ins uc ion on he A64FX occupies one en y in bo h he load and he s o e bu e . While SKX implemen s a uni ied ese a ion s a ion (RS) wi h 97 en ies, bo h he A64FX and Rome ha e se e al smalle RS. The A64FX di ides i s ese a ion s a ion in o 2 ×20 en ies o 2 in ege , loa ing-poin , and SIMD pipelines, 2 ×10 en ies o 2 add ess calcula ion pipelines, and 19 en ies o he b anch pipeline. Rome’s ese a ion s a ion has 4 ×16 en ies o 4 in ege pipelines (scala +SIMD), 28 en ies o 3 add ess calcula ion pipelines, and 36 en ies o 4 loa ing-poin pipelines (scala +SIMD). 5. A m po ing o genomics ke nels Mos ke nels p esen ed in Sec ion 3 a ge x86 a chi ec u es and ha e no been ex ensi ely es ed no op imized o A m machines. Thus, i was expec ed ha some ke nels could un in o ailu es and e en gene a e inco ec esul s. To e i y he execu ion o he ke nels, we used he SKX sys em o compu e he co ec ou pu o all ke nels and inpu s (i.e., g ound u h). Fo ou expe imen s, we used he GNU compile (GCC) on G a i on3 ( 11.2.0), SKX ( 10.1.0), and Rome ( 10.2.0). On he A64FX, we used GCC ( 10.2.0) and he Fuji su Compile (FCC) ( 4.2.0b). Fo mos ke nels, FCC-compiled bina ies exhibi ed be e pe o mance. The Fu- ji su Compile implemen s wo compila ion modes: a adi ional mode (T ad) based on compile s o ea lie sys ems and a Clang mode based on Clang/LLVM. In all cases, we ob ained be e execu ion imes when compiling wi h FCC’s Clang mode. We lacked FCC-compiled e sions o key-op imized Py hon lib a ies. Fo hese easons, all he esul s p esen ed in his documen o he A64FX ha e been ob ained using he Clang mode o FCC, excluding he wo Py hon ke nels (NN-BASE and NN-VARIANT), whose lib a ies we e compiled using GCC. We compile all ke nels wi h a leas -O2 op imiza ion le el and enable CPU-speci ic op imiza ions: -ma ch=a m 8-a+s e on he A64FX, -mcpu=na i e on G a i on3 and -ma ch=na i e on SKX and Rome. Enabling CPU-speci ic op imiza ions in ABEA and POA esul ed in inco ec execu ions, p obably due o p og amming e o s in he o iginal sou ce code. The e o e, such op imiza ions a e no used o hese wo ke nels. A e pe o ming he app op ia e modi ica ions o he ke nels so all o hem success ully execu e on A m, we applied u he op i- miza ions o some ke nels o imp o e he pe o mance ob ained in his a chi ec u e. Such op imiza ions a e desc ibed in he ollowing subsec ions. Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 319 L. López-Villellas e al. Fig. 2. Speedup o SIMD ke nels o e hei scala e sion on he expe imen al se up using he la ge inpu s. 5.1. Exploi ing ec o iza ion Some ke nels implemen x86- ec o ized e sions o hei mos ime- consuming pa s. In pa icula , BSW and FAST-CHAIN include AVX2 and AVX512 e sions o hei c i ical unc ions using in insics. Simi- la ly, POA implemen s SIMD e sions o i s code using AVX2-in insics and SIMD E e ywhe e (SIMDe). We ha e implemen ed SVE-in insics e sions o FAST-CHAIN, BSW, and WFA and a Neon-in insics e sion o BPM. SIMDe does no ully suppo SVE ye , so we could no le e age POA’s SIMDe e sion. Fig. 2 shows he speedup o ec o ized ke nels o e hei scala e sion on he expe imen al se up using he la ge inpu o he ke nels. No e ha he SVE ec o leng h o G a i on3 (256 bi s) is hal he A64FX’s (512 bi s), and he e o e he pe o mance speedups o SVE ke nels o e hei scala e sions a e mo e modes in G a i on3. BPM: The co e idea behind ec o izing BPM is o ans o m he alignmen ope a ions used o ill he dynamic p og amming able in o simple machine-wo d ope a ions. These simple ope a ions a e in ege addi ions, bi shi s, and bi wise ORs and ANDs. This way, a ious dynamic p og amming cells a e bi -packed wi hin a machine wo d and i s dependencies a e encoded using bi -wise ope a ions. In packed SIMD, ec o ope a ions a e pe o med in independen packe s wi h a maximum wid h equal o he machine’s maximum wo d wid h, a he han a whole bi ec o (i.e., i is no possible o pe o m a 128-bi wid h ope a ion in a 64-bi double wo d machine). Fo example, when pe o ming a le -shi ope a ion, he le mos bi o each wo d is los . Howe e , in o de o ec o ize BPM we would wan his bi o be appended o he closes -le wo d, e ec i ely pe o ming a ec o -wid h le -shi ope a ion. To ci cum en his p oblem, we mus pe o m addi ional ope a ions o manually ca y ha bi o he co ec posi ion. The numbe o addi ional ope a ions equi ed by his app oach o wo k scales wi h he ec o leng h. Thus, we decided o e alua e he po en ial o he ec o e sion o BPM using he Neon ec o ex ension (128-bi ec o s). The ec o ized loop execu es 1.7× mo e ins uc ions han he o iginal bu pe o ms 2× ewe i e a ions. On he A64FX, SIMD e sions o simple ins uc ions, like in ege addi ion, we e much mo e expensi e han scala ones. Fo example, a simple 64-bi addi ion akes one cycle, while a ec o addi ion o wo 64-bi wo ds akes ou cycles. This di e ence in la encies leads o a slow-down o 2×. G a i on3 has lowe SIMD la encies. Howe e , he inc ease in he numbe o ins uc ions in he loop leads o a 30% pe o mance loss. Since we did no gain any pe o mance using he Neon e sion, i was disca ded in a o o he o iginal scala code. We belie e ha an in e -sequence o coa se-g ain app oach (i.e., pe - o m he sequence alignmen o se e al sequences simul aneously) will deli e be e pe o mance since i simpli ies he ec o iza ion. BSW: The SVE e sion o BSW [70] is a ansla ion o A m SVE- in insics o he x86- ec o e sion ound in BWA-MEM2, which g oups he sequence alignmen o mul iple equal-leng h sequences ia SIMD in- s uc ions (i.e., in e -sequence ec o iza ion). The x86-in insics e sion o BSW elies on masks and blend ope a ions o selec alid en ies om he ec o egis e s. The SVE e sion akes ad an age o SVE’s p edica e ins uc ions o a oid he need o blend ope a ions, e ec i ely educing he numbe o o al ins uc ions execu ed. BSW uses in ege s o 16 bi s, allowing o p ocess 32 elemen s pe i e a ion using SVE-512 (A64FX) and 16 using SVE-256 (G a i on3). The SVE e sion o BSW pe o ms 3.4× and 1.3× as e han i s scala e sion on he A64FX and G a i on3, espec i ely. FAST-CHAIN: Ou SVE implemen a ion o FAST-CHAIN is a ansla- ion o SVE in insics o he x86 e sion. The o iginal x86 implemen a- ion o FAST-CHAIN execu es i s main loop scala e sion (i.e., a oids execu ing he ec o ized loop) when he numbe o i e a ions o pe - o m is small. Addi ionally, as usual in x86 ec o loops, i implemen s a loop- ail o p ocess he emaining elemen s. Since SVE is ec o -leng h agnos ic, we could a oid mos o he logic o he x86 e sion, educing he numbe o pe o med ins uc ions. The x86 ec o ized e sion o FAST-CHAIN uses 32 bi s ancho s. In some cases, 32-bi ancho s a e no su icien , and his ke nel gene a es inco ec esul s. To sol e his, we ha e implemen ed 64 and 32 bi s SVE e sions o FAST-CHAIN. The 64 bi s e sion always ou pu s co - ec esul s, bu we ha e used he 32 bi s implemen a ion o compa e agains he 32 bi s x86 implemen a ion. GenA chBench’s SVE e sion o FAST-CHAIN uns 4.5×and 1.8× as e han i s scala e sion (CHAIN wi hou heu is ics) on he A64FX and G a i on3, espec i ely. Expe imen al esul s show ha he pe - o mance o FAST-CHAIN compa ed o egula CHAIN g ea ly depends on he inpu used— he usage o heu is ics may lead o pe o mance a ia ions based on he cha ac e is ics o he inpu . Fo ins ance, us- ing GenA chBench’s la ge inpu , ou SVE e sion o FAST-CHAIN is 2.2× as e han egula CHAIN on he A64FX, bu i p esen s a 1.4× slowdown on G a i on3. WFA: The Wa e on Alignmen Algo i hm consis s o wo ope a- ions: compu e he nex wa e on (nex ope a ion) and ex end all he a hes - eaching poin s o a wa e on by exac ma ching cha ac e s om wo s ings (ex end ope a ion). The nex ope a ion can be au- oma ically ec o ized by he compile due o i s simple compu a ional pa e n. In con as , he ex end ope a o canno be au oma ically ec- o ized as each diagonal equi es an i egula amoun o compu a ions. To his end, we ha e ec o ized he ex end ope a ion using a cus om implemen a ion elying on SVE in insics. Each ec o lane ex ends a di e en diagonal, compa ing ou bases pe lane un il a misma ch is ound. Because each diagonal equi es a di e en numbe o cha ac e compa isons, some lanes can equi e mo e i e a ions han o he s. We ackle his p oblem by masking he lanes as hey inish he ex ension p ocess. This way, se e al diagonals a e ex ended in pa allel. The SVE e sion o WFA deli e s a 1.6× and 1.25× speedup o e i s scala e sion on he A64FX and G a i on3, espec i ely. 5.2. Op imized lib a ies Many HPC ke nels and ools ely on equen ly used lib a ies. I is common o endo s, such as A m, Fuji su, o In el, o de elop op imized e sions o widely used unc ions and lib a ies a ge ing hei sys ems and a chi ec u es. Fo genome da a analysis, some ools exploi neu al ne wo ks (NNs) o imp o e he quali y o hei analysis and e- sul s. Fo he GenA chBench, we ha e es ed di e en implemen a ions o he lib a ies used by he NN-BASE and NN-VARIANT ke nels. NN-BASE: The NN-BASE ke nel builds upon he PyTo ch lib a y [57]. On he A64FX we ha e used an op imized e sion o PyTo ch o his speci ic CPU p o ided by Fuji su. On G a i on3, we ied wo di e en PyTo ch backends: PyTo ch compiled wi h OpenBLAS ( ecommended by A m) and PyTo ch compiled wi h oneDNN op imized wi h ACL (labeled as expe imen al). The docke images wi h he wo backends a e a ailable in [71]. The oneDNN-ACL backend pe o med 6.5×be e han he OpenBLAS one, and he e o e, we used i o ou expe imen s. On SKX, we used an op imized e sion o PyTo ch ha exploi s he AVX512 ec o ex ension. On Rome, we used an op imized Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 320 L. López-Villellas e al. Fig. 3. Single-co e ( op) and mul i-co e (bo om) execu ion ime o GenA chBench’s ke nels on he expe imen al se up. Mul i-co e esul s co espond o execu ions using all a ailable co es on each machine: 48 h eads on he A64FX and SKX and 64 h eads on G a i on3 and Rome. The esul s a e no malized o he pe o mance on he A64FX using one co e ( op) and 48 co es (bo om). FCHAIN, KCNT, NNB and NNV a e he abb e ia ions o FAST-CHAIN, KMER-CNT, NN-BASE and NN-VARIANT, espec i ely. NN-VARIANT is no aken in o conside a ion o he a e age in he mul i-co e plo . PyTo ch e sion ha suppo s he AVX2 ec o ex ension a ailable on he machine. NN-VARIANT: The o iginal NN-VARIANT ke nel om Genomics- Bench is based on Clai [25] a ian calle . In u n, his a ian calle e- lies on Tenso Flow [58]. Clai uses Tenso Flow 1 while Fuji su p o ides an op imized e sion o Tenso Flow 2 o he A64FX. Fo ha eason, we decided o use Clai 3 [26] ins ead, an upda ed e sion o Clai ha elies on Tenso Flow 2. To execu e using GenA chBench’s inpu s, we used he Ox o d Nanopo e 941_p om_hac_g360+g422 [60] p e- ained model om Clai 3. On G a i on3, we es ed h ee di e en Tenso Flow backends: Tenso Flow compiled wi h oneDNN op imized wi h ACL, using Tenso Flow’s Eigen h ead-pool o pa allelism ( ecom- mended by A m); Tenso Flow compiled wi h oneDNN op imized wi h ACL, using ACL’s schedule ; and Tenso low compiled wi h he Eigen backend. The docke images wi h he h ee backends a e a ailable in [72]. The Eigen backend pe o med mo e han 1.6×be e han he o he and he e o e i was he one used o un ou expe imen s. We used op imized Tenso Flow e sions on SKX and Rome capable o exploi ing he AVX512 and AVX2 ec o ex ensions. 5.3. Algo i hmic and code op imiza ions This sec ion p esen s he algo i hmic and code op imiza ion we pe o med o imp o e he pe o mance o FMI and KMER-CNT. FMI: GenA chBench’s FMI e sion implemen s h ee op imiza ions p oposed by Langa i a e al. [70]. One o he mos called unc ions in his ke nel is backwa dEx . To educe he o e head o he calls, his unc ion is always o ced o be in-lined. FMI uses he buil in_popcoun unc ion. This unc ion coun s he numbe o bi s se o one in an in ege . None o he es ed compile s ansla es his unc ion o SVE’s popula ion coun ins uc ion. Ins ead, hey use bi wise ope a ions and masks. To o ce exploi ing SVE capabili ies, all calls o buil in_popcoun a e eplaced by SVE in insics. FMI pe o mance is hea ily a ec ed by memo y access la encies. To hide hese la encies, he op imized e sion o FMI in e lea es he execu ion o se e al sequences, e ec i ely pe o ming se e al memo y accesses in pa allel. By applying he h ee p esen ed op imiza ions, we imp o ed he ke nel pe o mance on bo h A m machines by oughly 35%. KMER-CNT: Ou expe imen al e alua ion shows ha he pe o - mance o his ke nel is hea ily a ec ed by h ead mig a ions. To a oid h ead mig a ions, we po ed KMER-CNT om he P h eads lib a y o OpenMP and se OMP_PROC_BIND clause o ue be o e execu ions. This change led o mo e han 4×speedups on bo h x86 machines when using all a ailable co es. Howe e , he pe o mance o he A m machines emained he same. KMER-CNT elies on wo global da a s uc u es o s o e he numbe o indi idual k-me s: an a ay o 4-bi coun e s and libcuckoo’s [55] mul i- h ead hash-map, which s o es 64-bi coun e s. Each en y o he global a ay is an 8-bi a omic in ege , which is spli in hal o c ea e wo 4-bi coun e s. The a ay coun e s a e upda ed using a omic compa e-and-swap ope a ion. Once he 4-bi coun e o a k-me sa u a es, he ollowing inc emen s a e pe o med in he global hash map, also elying on a omic compa e-and-swaps o upda e i s coun e s. E en by a oiding h ead mig a ions, he scalabili y o he o iginal ke nel was poo on all he machines. I achie ed a maximum o 7×and 5× s. se ial execu ion on he A64FX (48 h eads) and G a i on3 (64 h eads), espec i ely. We decided o implemen wo new app oaches o y o imp o e pa allel pe o mance. The applica ion di ides he inpu be ween he a ailable h eads. Each h ead i e a es h ough he k-me s o i s pa o he inpu and inc emen s he coun e o he ead k-me s in he global a ay o hash map. Since he inpu is ead sequen ially and he e is almos no compu a ion o pe o m, mos o he execu ion ime is spen accessing he global coun e s in mu ual exclusion. To educe con en ion and imp o e da a locali y, ou i s app oach assigns pa o he inpu o each h ead, and all h eads ead he ull inpu bu only coun pa o he k-me s. This way, ins ead o ha ing a single global a ay and hash map, each h ead can ha e a smalle ins ance o he da a s uc u es and access hem wi hou con en ion. The o iginal e sion o he ke nel uses compa e-and-swap ins ead o e ch-and-add o upda e he coun e s because each 8-bi en y o he a ay s o es wo 4-bi coun e s. In o de o limi memo y usage, when he k-me size is g ea e han 17, he ke nel does no ins an ia e he global a ay, and all he coun ing akes place on he hash-map. Fo he maximum allowed k-me size (17), we equi e 217 4-bi en ies in he global a ay, esul ing in 8 GB o memo y. Since we ha e mo e han enough memo y in all sys ems, ou second app oach uses 8-bi ins ead o 4-bi coun e s, doubling he memo y equi emen s. This enables e ch-and-add usage and educes he numbe o accesses o he hash map. The single h ead execu ion ime o he ke nel did no change wi h any o he new e sions. Ou i s app oach (p i a e s uc u es) equally Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 321 L. López-Villellas e al. di ides he possible k-me s be ween h eads. Howe e , some k-me s a e mo e common in he inpu , causing load imbalance be ween h eads de i ing in e en poo e scalabili y han he o iginal ke nel. The second app oach ( e ch-and-add) imp o es he ke nel’s scalabili y on all he machines: i uns 2.5×,1.4×, 3×and 2.3× as e han he o iginal e sion on he A64FX (48 h eads), G a i on3 (64 h eads), SKX (48 h eads) and Rome (64 h eads), espec i ely. Consequen ly, we used he e ch-and-add app oach o he es o he expe imen s. 6. Pe o mance cha ac e iza ion This sec ion p esen s a de ailed pe o mance cha ac e iza ion o he ke nels in ou expe imen al se up. We use he op imized e sions o he ke nels desc ibed in Sec ion 5. Fo all o he s udies p esen ed, we ha e anno a ed he code o he ke nels o de ine hei egion o in e es , i.e., we only s udy he pa o he ke nels dedica ed o meaning ul compu a ion. All he esul s shown in his sec ion ha e been compu ed using he la ge inpu o each ke nel. We no ed minimal a ia ion be ween execu ions o he ke nels, wi h a maximum ela i e s anda d de ia ion o 5% obse ed ac oss 10 epe i ions o he expe imen s. Consequen ly, we showcase he esul s based on a single execu ion in he igu es. While execu ing DBG wi h high h ead coun s in G a i on3, ou lie execu ion imes occu ed app oxima ely 10% o he ime. In he case o DBG in G a i on3, we selec i ely p esen esul s om an inlie execu ion. 6.1. Single- h ead pe o mance The op plo o Fig. 3 shows he single- h ead execu ion ime o each ke nel on he expe imen al se up. The esul s a e no malized o he pe o mance on he A64FX (see Table 3 o he supplemen a y ma e ial o he execu ion imes o he ke nels). The A64FX ea u es signi ican ly ewe ou -o -o de esou ces, a smalle memo y hie a chy, and highe memo y la encies han he es o he sys ems. On a e age, he o me is 2.4×, 1.8×, and 1.7× slowe han G a i on3, SKX and Rome on single- h eaded execu ions, espec i ely. Exploi ing he SVE capabili ies o he A64FX helps o educe his slowdown. SVE ec o ized ke nels (BSW, FAST-CHAIN, and WFA) p esen be e - han-a e age pe o mance on he A64FX: BSW pe o mance is simila o he exhibi ed on G a i on3 and only 17% wo se han he pe o mance on he x86 machines, FAST-CHAIN pe o ms be e han on Rome, and WFA pe o ms be e han on SKX. No e ha BSW and FAST-CHAIN exploi AVX512 on SKX while hey le e age AVX2 on Rome, and ha WFA is no ec o ized on he x86 machines. The deep-lea ning ke nels (NN-BASE and NN-VARIANT) a e he wo s -pe o ming on he A64FX. G a i on3 pe o ms excep ionally well in single- h ead execu ions. On a e age, i p esen s 2.44×, 1.33×, and 1.39×pe o mance speedups wi h espec o he A64FX, SKX and Rome, espec i ely. FAST-CHAIN pe o mance on G a i on3 is 1.8×be e han on Rome (AVX2) bu 70% wo se han on SKX since i exploi s AVX512 (512 bi s) on ha machine. WFA uns 2.5×and 1.8× as e on G a i on3 han on SKX and Rome, espec i ely. In con as o he A64FX, he deep-lea ning ke nels (NN-BASE and NN-VARIANT) deli e good pe o mance on G a i on3, showing speedups o be ween 3.1–6.2×compa ed o he A64FX. 6.2. Pa allel pe o mance We e alua e he pa allel pe o mance o GenA chBench’s ke nels using di e en h ead coun s: 2, 8, 24, 48, and 64. The A64FX and SKX implemen 48 co es. Hence, execu ions wi h mo e han 48 h eads ha e only been pe o med on G a i on3 and Rome. Con olling h ead a ini y was manda o y in ou expe imen s o achie e good pa allel pe o mance on he machines, especially on he A64FX. Fo mos ke nels, all he execu ions we e pe o med by binding h eads o co es. Fig. 4. Speedup o e se ial execu ion o GenA chBench’s ke nels on he expe imen al se up. We show he achie ed speedup using di e en h ead coun s: 2, 8, 24, 48, and 64. The A64FX and SKX 64- h eads poin s a e no shown in he igu e, since hose machines only implemen 48 co es. ABEA, NN-BASE, and NN-VARIANT do no allow ull h ead a ini y con ol. The e o e, h ead mig a ions can occu in hese h ee ke nels. Fig. 4 shows he speedup o e se ial execu ion achie ed by he ke nels on he expe imen al se up using he p e iously p esen ed h ead coun s. Addi ionally, he bo om plo o Fig. 3 compa es he pe o - mance ob ained using all a ailable co es on each machine: 48 h eads Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 328 L. López-Villellas e al. [89] H. 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Bio echnol. Biomed. 07 (01) (2024) h p://dx.doi.o g/ 10.26502/jbb.2642-91280134. [95] T. Dunn, e al., SquiggleFil e : An accele a o o po able i us de ec ion, in: MICRO-54: 54 h Annual IEEE/ACM In e na ional Symposium on Mi- c oa chi ec u e, MICRO ’21, ACM, 2021, h p://dx.doi.o g/10.1145/3466752. 3480117. [96] P.J. Shih, e al., E icien eal- ime selec i e genome sequencing on esou ce-cons ained de ices, GigaScience 12 (2022) h p://dx.doi.o g/10.1093/ gigascience/giad046. [97] T. Robinson, e al., Ha dwa e accele a ion o genomics da a analysis: chal- lenges and oppo uni ies, Bioin o ma ics (2021) 1–11, h p://dx.doi.o g/10. 1093/bioin o ma ics/b ab017. Lo ién López-Villellas is a Ph.D. s uden a he Uni e si y o Za agoza. His esea ch ocuses on exploi ing no el and consolida ed pa allel and ec o a chi ec u es o scien i ic applica ions, such as molecula dynamics and genomics. P io o s a ing his Ph.D., he wo ked as a esea ch enginee a he Ba celona Supe compu ing Cen e o wo yea s. He holds a BSc in compu e science om he Uni e si y o Za agoza and a MSc in High-Pe o mance Compu ing om he Uni e si a Poli ècnica de Ca alunya. Rubén Langa i a-Bení ez ecei ed his B.S. deg ee in com- pu e science om Uni e sidad de Za agoza in 2018. He spen one academic yea as an E asmus s uden a he Uni e si y College Co k. His inal deg ee p ojec was abou op imizing molecula dynamics applica ions. He ecei ed MS deg ee om UPC in Janua y 2021. He is cu en ly wo k- ing a he BSC as a esea ch s uden . His esea ch in e es s include p ocesso mic oa chi ec u e and HPC applica ions. Asa Badouh is a esea ch so wa e enginee wi h a pas- sion o high-pe o mance compu ing and machine lea ning. O e he las 4.5 yea s, Asa has been wo king a he Ba celona Supe compu ing Cen e as a Resea ch Enginee , ocusing on genomics and heal hca e applica ions. P io o ha , Asa wo ked o 6 yea s as an in e n and enginee in he In el compile R&D eam in Is ael, de eloping LLVM- based compile s o OpenCL and C/C++. Aside om ha , Asa holds a bachelo ’s deg ee in compu e science om he Technion, Is ael; and a mas e ’s deg ee in Da a Science om Uni e si a Poli ècnica de Ca alunya. Víc o So ia-Pa dos ecei ed a B.Sc. in compu e science om Uni e si a de Za agoza, in 2019. He ecei ed an M.Sc. in compu e science om Uni e si a Poli ècnica de Ca alunya (UPC), in 2022. He is cu en ly a second-yea Ph.D. in compu e a chi ec u e wi h he UPC. He also wo ks as a esea che in he Ba celona Supe compu ing Cen e , wi hin he Cen e o Excellence pa ne ship wi h A m. His esea ch in e es s include high-pe o mance compu ing a chi ec u es, cache cohe ence, and mul ico e a chi ec u es. Quim Aguado-Puig ecei ed a B.Sc. deg ee in compu e sci- ence in 2019 om he Uni e si a Au ònoma de Ba celona (UAB). He ecei ed an MSc a he Uni e si a Poli ècnica de Ca alunya (UPC) in 2023. He is cu en ly a i s -yea Ph.D. s uden a UAB. He has p e iously wo ked as a esea ch enginee in he p ojec Designing RISC-V-based Accele a o s o nex -gene a ion Compu e s (DRAC) a UAB in collab- o a ion wi h he Ba celona Supe compu ing Cen e (BSC). His esea ch in e es s include high-pe o mance compu ing, massi ely pa allel a chi ec u es, and GPU p og amming; wi h applica ions o genomics, compu a ional biology, and sequence alignmen . Guillem López-Pa adís ecei ed a B.Sc. and M.Sc. om Uni e si a Poli ecnica de Ca alunya (UPC) in 2017 and 2020, espec i ely. He is cu en ly a hi d yea Ph.D. s uden in Compu e A chi ec u e a UPC and Ba celona Supe compu ing Cen e (BSC). He ac i ely pa icipa es in di e en Eu opean p ojec s, as well as in in e na ional collabo a ions wi h academia and indus y. He has al- eady published some pape s in in e na ional con e ences and pa icipa ed in di e en apeou s designing powe - e icien RISC-V p ocesso s. His esea ch in e es s include high-pe o mance compu ing a chi ec u es, scaling RTL Sim- ula ions, and domain-speci ic accele a o s, wi h special emphasis on cohe en in e connec s be ween co es and ha dwa e accele a o s. Max Doblas ecei ed a B.Sc. in elec ical enginee ing and compu e science om Uni e si a Poli ècnica de Ca alunya (UPC), in 2020. He ecei ed an M.Sc. in compu e sci- ence om UPC, in 2021. He is cu en ly a second-yea Ph.D. in compu e a chi ec u e wi h he UPC. He also wo ks as a Resea ch Enginee in he p ojec Designing RISC-V-based Accele a o s o nex -gene a ion Compu e s (DRAC) a he Ba celona Supe compu ing Cen e (BSC), in which he has designed a powe -e icien p ocesso wi h se e al ex ensions o domain-speci ic applica ions. His esea ch in e es s include high-pe o mance compu ing a chi ec u es, and domain-speci ic accele a o s, wi h appli- ca ions o genomics, compu a ional biology, and sequence alignmen . Ja ie Se oain ecei ed his Ph.D. in compu e science and enginee ing om he Complu ense Uni e si y o Mad id (UCM), wo king on wo kload op imiza ion o GPUs. A e wo king as a pos -doc a he Spanish Na ional Cen e o Bio echnology (CNB), and a esea ch enginee a A m Resea ch, he is cu en ly a senio membe o echnical s a in AMD Resea ch and Ad anced De elopmen , wo king on compile esea ch o AI accele a o s. His esea ch in e es s cen e a ound compu e accele a o s and specialized a chi- ec u es, and his cu en esea ch is ocused on au oma ed ML wo kload op imiza ions o spa ial a chi ec u es. Chulho Kim is P incipal Consul an wi h he Leno o In as uc u e Solu ions G oup Se ices in Uni ed S a es. Chulho has a Bachelo o Science deg ee in Ma hema - ics o Compu a ion om UCLA in 1989 and joined IBM Kings on. He joined Leno o US in 2014. He has wo ked in High Pe o mance Compu ing (HPC) since 1993. He likes o apply his skills o debug ex emely complex issues, anging om applica ion pe o mance o sys em and ne - wo king pe o mance issues. He is esponsible o unning Top500/G een500 on cus ome clus e s o Leno o (#1 G een500 en y since No embe 2022). Mako o Ono ecei ed he ME in biophysics om he Osaka Uni e si y in 1985 and joined IBM Tokyo Resea ch lab. He wo ked on compu e g aphics esea ch hen s a ed sys em a chi ec u e de elopmen in IBM Sys em x de elopmen . He is cu en ly a Dis inguished Enginee a Leno o In as- uc u e Solu ions G oup and is a lead a chi ec o edge compu ing. His in e es s include edge compu ing, edge AI, and he e ogeneous and al e na i e a chi ec u e including non adi ional CPU / GPU a chi ec u e. Fu u e Gene a ion Compu e Sys ems 157 (2024) 313–329 329 L. López-Villellas e al. Ad ià A mejach is a Lec u e P o esso in Compu e A - chi ec u e a Uni e si a Poli ècnica de Ca alunya (UPC), and associa e esea che a he Ba celona Supe compu ing Cen e (BSC). He ecei ed his Ph.D. om UPC in 2014 and hen s a ed his esea ch ca ee a BSC, whe e he lead he echnical con ibu ions o mul iple FP7 and H2020 p ojec s. His esea ch in e es include memo y sys ems, he e oge- neous a chi ec u es, simula ion me hodologies and ec o a chi ec u es. Cu en ly he leads a g oup ha o e sees all he a chi ec u al simula ion e o s a BSC, enabling esea ch on mul iple compu e a chi ec u e opics. He has published mo e han 30 well- anked in e na ional con e ence and jou nal pape s. San iago Ma co-Sola ecei ed he M.Sc. and Ph.D. deg ees in compu e science om he Uni e si a Poli ècnica de Ca alunya (UPC) in 2012 and 2017, espec i ely. Du - ing his Ph.D., he wo ked a he Algo i hm De elopmen and Bioin o ma ics G oup a Spanish Na ional Cen e o Genome Analysis (CNAG) and lec u ed a he Uni e si a Au ònoma de Ba celona (UAB). He is cu en ly a Senio Resea che a he Ba celona Supe compu ing Cen e (BSC) and a Lec u e a he UPC. His esea ch in e es s include high-pe o mance compu ing, he e ogeneous a chi ec u es, genome-da a analysis, and algo i hms in bioin o ma ics and compu a ional biology. Jesús Alas uey-Benedé ecei ed he M.S. deg ee in Telecommunica ion and he Ph.D. deg ee in Compu e Sci- ence om Uni e sidad de Za agoza in 1997 and 2009, espec i ely. He is an associa e p o esso in he Compu e Science and Sys ems Enginee ing Depa men (DIIS), Uni- e sidad de Za agoza, Spain. His esea ch in e es s include p ocesso mic oa chi ec u e, memo y hie a chy, and HPC applica ions. Pablo Ibáñez ecei ed he M.S. deg ee in compu e science om he Uni e si a Poli écnica de Ca alunya, Spain, in 1989, and he Ph.D. deg ee in compu e science om he Uni e sidad de Za agoza, Spain, in 1998. He is an associa e p o esso wi h he Compu e Science and Sys ems Engi- nee ing Depa men , Uni e si y o Za agoza. His esea ch in e es s include p ocesso mic oa chi ec u e, memo y hie - a chy, pa allel compu e a chi ec u e, and high pe o mance compu ing applica ions. Miquel Mo e ó ecei ed he B.Sc., M.Sc., and Ph.D. de- g ees om Uni e si a Poli ècnica de Ca alunya (UPC), Spain. Cu en ly, he is a Ramón y Cajal Fellow a UPC Ba celona. P io o joining UPC, he spen 5 yea s as a Senio Resea che wi h he Ba celona Supe compu ing Cen- e (BSC), Spain, and 15 mon hs as a pos -doc o al ellow wi h he In e na ional Compu e Science Ins i u e (ICSI), Be keley. His esea ch in e es s include high pe o mance compu e a chi ec u es, domain-speci ic accele a o s and ha dwa e-so wa e co-design o u u e massi ely pa allel sys ems.