Supporting the Xeon Phi coprocessor in a Heterogeneous Programming Model

Suppo ing he Xeon Phi cop ocesso in a He e ogeneous P og amming Model Ana Mo e on-Fe nandez, Edua do Rod iguez-Gu iez, A u o Gonzalez-Esc ibano, and Diego R. Llanos Depa amen o de In o m´a ica, Edi . Tecn. de la In o maci´on, Uni e sidad de Valladolid, Campus Miguel Delibes, 47011 Valladolid, Spain. Abs ac . Supe compu e s a e becoming mo e he e ogeneous. They a e composed by se e al machines wi h di e en compu a ion capabili ies and di e en kinds and amilies o accele a o s, such as GPUs o In el Xeon Phi cop ocesso s. P og amming hese machines is a ha d ask, ha equi es a deep s udy o he a chi ec u al de ails, o exploi ing e icien ly each compu a ional uni . In his pape , we p esen an ex ension o a he e ogeneous p og amming model, in o de o also suppo In el Xeon Phi cop ocesso s. This con i- bu ion ex ends an exis ing he e ogeneous p og amming lib a y, by aking ad an age o bo h he GPU communica ion model and he CPU execu- ion model o he o iginal lib a y. Ou expe imen al esul s show ha using ou app oach, he p og amming e o needed o changing he a - ge de ices is highly educed, by o example eusing he 97% o he code be ween a GPU implemen a ion and a Xeon Phi implemen a ion o he Mandelb o benchma k. 1 In oduc ion and Backg ound Suppo ing compu a ional accele a o s such as GPUs o Xeon Phi cop ocesso s in he s a e-o - he-a p og amming models is i al o exploi cu en Supe - compu e s. They a e composed by se e al machines wi h di e en compu a ion capabili ies and di e en kinds and amilies o accele a o s, as we obse e o example in he con igu a ion o he TOP500 supe compu e s [16]. Howe e , p og amming solu ions o an e icien deploymen in his kind o de ices is a e y complex ask, ha elies on he manual managemen o memo y ans e s and con igu a ion pa ame e s. The p og amme has o ca y ou a deep s udy o he pa icula da a needed o be compu ed a each momen , in di e en compu ing pla o ms, also conside ing a chi ec u al de ails o exploi e icien ly such execu ion sys ems [2]. Many wo ks add ess he p oblem o he e ogeneous sys ems managemen (e.g. [5, 14, 4]) ollowing wo al e na i es: gene a ing speci ic codes, o using un- ime lib a ies. Using cu en he e ogeneous code gene a o s, he code should be ecompiled o each di e en execu ion pla o m in o de o be e exploi he pe o mance capabili ies o he sys em. As o lib a ies, some wo ks, o speci ic kind o applica ions, add ess he po abili y p oblem using na i e p og amming models. Fo example, MAGMA lib a y [6] p o ides a uni ied p og amming en i- onmen o he e ogeneous sys ems using bo h CPUs and accele a o s, such as a GPU o In el Xeon Phi, o dense linea algeb a algo i hms. Howe e , mos o he e ogeneous lib a ies ely on he OpenCL abs ac ion. OpenCL [15] is a widesp ead p og amming amewo k o deal wi h he e ogeneous de ices. The OpenCL con ex abs ac ion allows he managemen o mul iple de ices o he same na u e (using he same pla o m in OpenCL no a ion). The abs ac ions in oduced by OpenCL ha e been p o ed ha p e en he ob aining o he same e iciency as when using di ec ly he endo p og amming models, o se e al common si ua ions [10]. As a esul , many s a e-o - he-a he e ogeneous ame- wo ks and lib a ies o highe le el o abs ac ion [9, 13, 8, 18, 3, 17], ha ely on OpenCL as execu ion laye , ypically inhe i some o hese p oblems. Addi ionally, du ing he las decade se e al high-pe o mance accele a o p e- de ined lib a ies, such as cuBLAS [12] o MKL, ha e been de eloped using he endo speci ic p og amming models. Fo making he mos o such wo ks, i is ecommendable he use o such na i e o endo speci ic p og amming models and compile s o each di e en kind o de ice. In his pape , we ex end a he e ogeneous p og amming model ha is no based on he use o OpenCL. The o iginal p oposal, named Con olle [1], is a endo -speci ic, compile -agnos ic lib a y ha in oduces an abs ac en i y o allow he anspa en launching o se ies o asks on a GPU o on a CPU. I exploi s hei na i e o endo speci ic p og amming models, hus enabling he po en ial pe o mance ob ained by hem. In his wo k we p esen an ex ension o his Con olle he e ogeneous p og amming model ha includes he suppo o In el Xeon Phi cop ocesso s, known also unde he name o Many In eg a ed Co es (MICs). The model is based on he mix o he GPU communica ion model wi h he CPU execu ion model o he Con olle lib a y. We de elop a comple e un ime execu ion sys em ha includes me hods o ask launching, da a ans- e s be ween he MIC accele a o and he hos , and a queue sys em o manage he ke nel execu ions. I pe ec ly i s wi h he p e ious Con olle lib a y, hus s anda dizing and abs ac ing o he p og amme he issues ela ed o he di - e en accele a o p og amming. We also p esen an expe imen al s udy wi h ou s udy cases. We show ha ou app oach is highly lexible, wi h minimum p og amming e o o chang- ing he a ge de ices. Mo eo e , he pe o mance esul s show ha ou imple- men a ion does no in oduce signi ican pe o mance penal ies compa ed wi h e e ence codes. 2 P oposal: MIC Con olle model The wo k p esen ed in [1] p oposed a simple he e ogeneous p og amming model o deal wi h he hyb id-compu a ion- ela ed issues in an abs ac way o he p og amme . The model de ines an objec able o manage ei he a g oup o CPU co es o a GPU accele a o (see le o Fig. 1), using in e nally na i e p og amming echniques (OpenMP and CUDA espec i ely). CPU Exec. MIC Exec. Comm. GPU Comm. CPU Exec. GPU Comm. Old Con olle objec MIC Con olle objec Sha ed memo y CUDA Sha ed memo y CUDA Fig. 1. Le : P e ious con olle model, only suppo ing CPU and GPU. Righ : The MIC p oposed model, mixing he GPU-CPU model ea u es. In ou p oposal, we dis inguish wo pa s in he in e nal model, suppo ing each kind o compu a ional de ice: Execu ion and communica ion models. As he CPU-co e g oup de ices sha e he memo y space wi h he hos , he con olle objec only has o p o ide an execu ion model. On he o he hand, o GPUs, CUDA p og amming model p o ides a execu ion sys em o enqueue and launch ke nels wi h he p ope g anula i y le el ha is used in he Con olle model. The GPU implemen a ion o he execu ion model is s aigh o wa d, bu i p o- ides an in e nal mechanism o implemen policies and echniques o dealing memo y communica ion ope a ions ac oss di e en memo y spaces on hos and accele a o s (see le o Fig. 1). In his wo k we p opose o in eg a e MIC cop ocesso s in he Con olle model dis inguishing wo aspec s: The execu ion model and he memo y model. In e ms o execu ion, he Con olle model p oposed o g oups o CPU co e, ha blends blocks o ine g ained ke nels in o coa se CPU asks, is app op ia ed o Xeon Phi cop ocesso s. In e ms o memo y managemen , he abs ac model o da a communica ions needed o he MIC cop ocesso s is equi alen o he Con olle model o he GPU. Thus, suppo ing new kind o accele a o s, as he Xeon Phi, can be done by combining exis ing models o execu ion, and memo y communica ions ac oss di e en spaces, independen ly. The applica ion o his idea leads o a homogeneous p og amming model o he e ogeneous sys ems wi h MIC cop ocesso s, whe e he issues ela ed o he di e en accele a o p og amming a e anspa en o he p og amme . In his wo k we show he implemen a ion o his idea in he Con olle , o suppo compu a ional de ices such as MIC cop ocesso s, GPUs, and g oups o CPU- co es, wi hou edesigning o changing he p og amming model. 3 Backg ound: Con olle model In his sec ion we desc ibe he Con olle model [1], he app oach upon we build ou MIC execu ion sys em. We also desc ibe Hi map [7], he lib a y used o he da a-s uc u es managing. 1/* Gene ic ke nel codes o any de ice */ 2KERNEL(Ma Add, 3, 3OUT, Hi Tile_ loa , C, IN, Hi Tile_ loa , A, IN, Hi Tile_ loa , B ){ 4 in x = h ead.x; in y = h ead.y; 5hi _ ileElem( C, 2, x, y ) = hi _ ileElem( A, 2, x, y ) + 6hi _ ileElem( B, 2, x, y ); 7} 8/* Hos p og am using he Con olle lib a y */ 9 in main(){ 10 in SIZE = 10000; 11 /* S age 1: Con olle c ea ion */ 12 Cn l comm; 13 Cn lC ea e(&comm, CNTRL_GPU, 0); 14 /* S age 2: Da a s uc u es c ea ion and ini ializa ion */ 15 Hi Tile_ loa A; Hi Tile_ loa B; Hi Tile_ loa C; 16 hi _ ileDomainAlloc( &A, loa , 2, SIZE, SIZE ); 17 hi _ ileDomainAlloc( &B, loa , 2, SIZE, SIZE ); 18 hi _ ileDomainAlloc( &C, loa , 2, SIZE, SIZE ); 19 ini Ma ices(&A, &B, &C); 20 /* S age 3: Da a s uc u es a achmen */ 21 Cn lA ach(&comm, &A); Cn lA ach(&comm, &B); Cn lA ach(&comm, &C); 22 /* S age 4: Ke nel launching */ 23 Th ead h eads; 24 Th eadIni ( h eads, 2, SIZE, SIZE ); 25 Cn lLaunch(comm, Ma Add, h eads, 3, &A, &B, &C); 26 /* S age 5: Da a s uc u es de achmen */ 27 Cn lDe ach(&comm, &C); 28 } Fig. 2. Ke nel de ini ion and con igu a ion, and hos p og am o a ma ix addi ion using he Con olle lib a y. 3.1 Hi map lib a y Hi map is he lib a y chosen o manage he pa i ion and mapping o da a s uc- u es. I is used in he Con olle model o manage he da a dis ibu ion ac oss de ices, and o p o ide a common in e ace o implemen da a managemen inside gene ic po able ke nels. Hi map de ines he Hi Tile s uc u e, an abs ac en i y o n-dimensional a ays and iles. A Hi Tile s uc u e is a handle o s o e a ay me a-da a, along wi h he poin e o he ac ual memo y space o he da a. The e a e only h ee unc ions o Hi map needed o wo k wi h he Con olle lib a y. The unc ion hi ileDomainAlloc is used o decla e and alloca e he index domains o a ile a ay. The unc ion hi ileF ee is used o ee he da a memo y and clean he handle . The unc ion hi ileElem is used in he hos o ke nel codes o access he elemen s o a ile. I ecei es a ile name, a numbe o dimensions, and he indexes alues o he desi ed elemen . The da a a e accessed in ow majo o de in all cases, independen ly o he implemen a ion. 3.2 Con olle model The Con olle model p o ides a s uc u ed p og amming me hodology oge he wi h se e al impo an ea u es: (1) A mechanism o de ine common ke nels eusable ac oss di e en ypes o de ices, o specialized ke nels o speci ic de- ice kinds; (2) A anspa en mechanism o memo y managemen , including op- imized communica ions o he da a s uc u es be ween he hos and he co e- sponding images in he accele a o s; (3) An op imiza ion sys em o selec p ope alues o ke nel-launching con igu a ion pa ame e s (such as he h eadblock geome y), guided by simple quali a i e code cha ac e iza ion p o ided by he p og amme . Ke nel de ini ions: In he Con olle lib a y, a ke nel is decla ed by using he p imi i e KERNEL < ype>. Whe e ype may be emp y o indica e a ke - nel usable on any kind o de ice, o a speci ic alue o a specialized code o a gi en ype o de ice. Cu en ly, he lib a y suppo s he speci ic dec- la a ions KERNEL GPU o CUDA code a ge ing NVIDIA’s GPUs, KERNEL CPU o hos machine code a ge ing se s o CPU co es, and KERNEL GPU WRAPPER, KERNEL CPU WRAPPER, o hos machine code which includes calls o specialized GPU o CPU lib a ies, such as cuBLAS o MKL ou ines. The ke nel-de ini ion p imi i es decla e in b acke s he numbe o pa ame e s o he ke nel, wi h a uple o in o ma ion o each pa ame e . The pa ame e in o ma ion includes i s ype, name, and inpu /ou pu ole. We see a ke nel de ini ion in lines 2-7 o Fig. 2. Con olle p og amming me hodology: A Con olle hos p og am ol- lows simple de elopmen guidelines: –The Con olle en i y c ea ion, assigning he compu a ional de ice o man- age by his con olle objec . A con olle en i y should be c ea ed o each compu a ional de ice ha will be used o compu a ion. –The a achmen o he da a s uc u es o he con olle objec . Da a s uc- u es ha will be accessed by a ke nel should be also p e iously a ached o he con olle en i y. –The launching o he compu a ional ke nels on he con olle objec . –The de achmen o he da a s uc u es. Figu e 2 shows a ma ix addi ion Con olle implemen a ion ha pe o ms he compu a ion on a GPU. In he main p og am, i s , a con olle objec is c ea ed, assigning a GPU o he objec (lines 12 o 13). Da a s uc u es a e c ea ed and ini ialized on he hos (lines 15 o 19). A e ha , hese da a s uc- u es a e a ached o he p e iously c ea ed con olle (line 21). In he s ep 4, he p og am launches he ke nel Ma Add. I uses a Th ead objec o speci y he numbe o h eads o be launched. In his example a h ead is launched o each each elemen o he ma ix C (lines 23 o 25). Finally, he p og am de aches he ma ix wi h he esul s (line 27). In his pape , we p opose a in e nal me hod in he p og amming model ha allows also he e icien execu ion o his p og am on he Xeon Phi, only by changing he CNTRL GPU modi ie by CNTRL XPHI on he line 13 o he code. 1/* In e nal a ach unc ion */ 2 oid a achToXPHI(Cn lXPHI* cn l, 3Hi Tile * ile){ 4Lock( ile, cn l); 5 in MIC= cn l->MIC; 6 loa *da a = ( loa *)(* ile).da a; 7 in numElems = hi _NumElem( ile); 8#p agma o load a ge (mic:MIC) 9in(da a:leng h(numElems) 10 alloc_i (1) 11 ee_i (0)) 12 13 } 1/* In e nal de ach unc ion */ 2 oid de achToXPHI(Cn lXPHI* cn l, 3Hi Tile * ile){ 4 in MIC= cn l->MIC; 5 loa *da a = ( loa *)(* ile).da a; 6 in numElems = hi _NumElem( ile); 7#p agma o load a ge (mic:MIC) 8in(da a:leng h(0) alloc_i (0) 9 ee_i (0)) 10 ou (da a:leng h(numElems) 11 alloc_i (0) 12 ee_i (1)) 13 Unlock( ile, cn l); 14 } Fig. 3. In e nal codes ha pe o m da a ans e s. Le : In e nal code o ans e he da a o a Hi Tile objec o a MIC cop ocesso om a hos . Righ : In e nal code o ans e he da a o a Hi Tile objec om a MIC cop ocesso o a hos . 4 In eg a ing Xeon Phi cop ocesso in he Con olle p og amming lib a y A p e ious e sion o he Con olle lib a y suppo s he deploymen o ke nels on GPUs o compu a ional de ices o med by g oups o CPU-co es. In his sec- ion we p esen he implemen a ion suppo o he MIC de ices in he Con olle lib a y. We implemen a MIC con olle objec con aining se e al unc ion- ali ies such as: an in e nal queue o manage he asynch onous ask execu ions, a a iable o s o e he MIC iden i ie ha he con olle objec is managing, o a me hod o lock and unlock da a s uc u es. 4.1 A aching and de aching da a s uc u es o MIC Con olle objec A aching a da a s uc u e o a Con olle objec implies o lock he da a s uc- u e un il ha s uc u e is de ached. In compu a ional de ices such as GPUs o MIC cop ocesso s, whe e hei memo y spaces a e sepa a ed o he hos memo y space, he a achmen /de achmen ope a ion also implies a da a ans e . We ha e implemen ed in his ex ension lib a y wo in e nal unc ions o pe - o m he da a ans e s o/ om he MIC cop ocesso , using he In el Language Ex ensions o O load (LEO). The unc ions a e execu ed in e nally when an a achmen o de achmen ope a ion is called espec i ely. Figu e 3 shows he code o bo h unc ions. On he le , we see he code used o a ach a ile o a MIC con olle objec ( ep esen ed in he igu e by he Cn lXPHI ype). In his unc ion, i s he a ached ile is locked. Secondly, he code ex ac s: 1) he MIC iden i ie assigned o he con olle objec (line 5); 2) he poin e o he ac ual da a (line 6); and 3) he numbe o elemen s o be ans e ed (line 7); A e ha , he unc ion pe o ms he ac ual da a ans e om he hos o he MIC, ensu ing ha he e is alloca ed memo y space in he a ge de ice (using alloc i (1)), and ha a e his o loading he ac ual da a will be main ained (using ee i (0)). On he igh , we show he code used o de ach a ile om a MIC con olle objec . As in he a achmen , i s he code ex ac s he in o ma ion abou he da a ans e (lines 4 o 6). Secondly, he ac ual da a ans e om he cop o- cesso o he hos is de ined using a p agma. Fo de e mining he poin e o he da a p e iously ans e ed, he p og am uses he in modi ie o make he da a poin e a ailable in he Xeon Phi, and se s he leng h o 0 o p e en any da a om being copied (lines 8 o 9). Once he poin e is a ailable on he MIC, he p agma also de ines he da a ans e and he eeing o he MIC space memo y (lines 10 o 12). Finally, he da a s uc u e is unlocked. 4.2 Ke nel de ini ions A ke nel de ini ion speci ies he de ice ha i s wi h i s implemen a ion by using he p imi i e KERNEL < ype>. We ha e de eloped a amewo k o suppo MIC ke nel de ini ions in he Con olle lib a y. A MIC ke nel de ini ion is ans- o med in h ee unc ions h ough mac os. We show he code o he h ee unc- ions in Fig. 5. Single-elemen unc ion: The i s unc ion implemen s he ke nel ha he p og amme has de ined o compu ing each elemen . The unc ion is named ke nel xphi ##name, whe e he ##name elemen is he i s pa ame e o he ke nel de ini ion. I is de ined as a MIC unc ion using he a ibu e a ge (mic). The pa ame e s a e a se o indexes ep esen ed by a Th ead objec , ha ep- esen a poin in he execu ion domain, and he ac ual ke nel pa ame e s. In Fig. 5, lines 4 o 5 show he unc ion decla a ion and lines 37 o 38 he unc ion de ini ion. Pa allel coa se-g ained unc ion: The second one (w appe xphi ##name) pe o ms he o loaded coa se-g ained pa allel compu a ion. I ecei es a a i- able numbe o pa ame e s. The i s one is he con olle objec , he second one he domain whe e compu a ion should be pe o med and he es a e he da a s uc u es needed o compu a ion. Lines 10 o 12 o Fig. 5 show how he in o ma ion is ex ac ed om he pa ame e s (auxilia y mac os o he ans- o ma ions a e de ined in Fig. 4). The nex o he body o he unc ion de ines he o load egion. The o load p agma ans e s he da a-s uc u e handle s, he domain ep esen ed by a Th ead objec , and he poin e o he ac ual da a o each Hi Tile. As in he de achmen ope a ion, in o de o de e mine he da a p e iously ans e ed, he o load p agma uses he in modi ie o make he da a poin e a ailable in he Xeon Phi, and se s he leng h o 0 o p e en any da a om being copied (see line 13 o Fig. 4). Inside he o load egion, he Hi Tile handle s upda e hei da a poin e o he ac ual ans e ed da a (line 15), and he pa allel compu a ion is pe o med on he speci ied domain (lines 16 o 28). Task addi ion unc ion: The hi d one is named name## xphi. I is he in e nal implemen a ion o a ke nel launch. In i s body, he unc ion implemen s he ask addi ion o he in e nal con olle queue. The in o ma ion needed o he addi ion is: The con olle objec , he poin e o he coa se-g ain pa allel 1/* Auxilia mac os o ke nels wi h one pa ame e */ 2 #de ine STRINGIFY(a) #a 3 #de ine XPHI_WRAPPER_PARAMS1(io1, ype1, alue1) 4 ype1 alue1 5 #de ine XPHI_WRAPPER_VALUES1(io1, ype1, alue1) 6 alue1 7 #de ine XPHI_WRAPPER_CAST1(io1, ype1, alue1) 8 ype1 alue1_p = ( ype1)a gs[2]; 9Hi Tile alue1_ = *(Hi Tile*) alue1_p; 10 loa *da a_ ile1=( loa *) ( alue1_ ).da a; 11 #de ine XPHI_OFFLOAD_PARAMS1(MIC, io1, ype1, alue1) 12 o load a ge (mic:MIC) in( h eads:leng h(3)) in( alue1_ ) 13 in(da a_ ile1:leng h(0) alloc_i (0) ee_i (0)) 14 #de ine XPHI_POINTERS1(io1, ype1, alue1) 15 Hi Tile alue1 = alue1_ ; 16 alue1.da a = da a_ ile1; Fig. 4. Auxilia y mac os de ined o a one ke nel pa ame e . 1/* Mac o o he ke nel de ini ion */ 2 #de ine KERNEL_XPHI(name, npa ams, pa ams...) 3/* Single-elemen unc ion decla a ion */ 4 s a ic oid __a ibu e__(( a ge (mic))) 5ke nel_xphi_##name(Th ead h eadId, XPHI_WRAPPER_PARAMS##npa ams(pa ams)); 6 7/* Pa allel coa se-g ained unc ion */ 8 s a ic inline oid w appe _xphi_##name( oid ** a gs){ 9 in MIC=cn l->MIC; 10 Cn lXPHI* cn l = (Cn lXPHI*) a gs[0]; 11 Th ead* h eads = (Th ead*)a gs[1]; 12 XPHI_WRAPPER_CAST##npa ams(pa ams); 13 _P agma( STRINGIFY(XPHI_OFFLOAD_PARAMS##npa ams(MIC, pa ams)) ) 14 { 15 XPHI_POINTERS##npa ams(pa ams); 16 _P agma("omp pa allel "){ 17 in i,j,k; 18 Th ead h eadId; 19 _P agma("omp o p i a e(i,j,k)") 20 o (i=0; i<= h eads->x; i++){ 21 o (j=0; j<= h eads->y; j++){ 22 o (k=0; k<= h eads->z; k++){ 23 h eadId.x = i; 24 h eadId.y = j; 25 h eadId.z = k; 26 ke nel_xphi_##name( h eadId, XPHI_WRAPPER_VALUES##npa ams(pa ams)); 27 } } } 28 }} 29 30 /* Task addi ion unc ion */ 31 oid name##_xphi(Cn lXPHI* cn l, Th ead h ead, 32 XPHI_WRAPPER_PARAMS##npa ams(pa ams)){ 33 Cn lXPHIAddTask(cn l, w appe _xphi_##name, h ead, npa ams, 34 XPHI_WRAPPER_VALUES##npa ams(pa ams)); 35 } 36 /* Single-elemen unc ion de ini ion */ 37 s a ic oid __a ibu e__(( a ge (mic))) 38 ke nel_xphi_##name(Th ead h eadId, XPHI_WRAPPER_PARAMS##npa ams(pa ams)); Fig. 5. Func ions in e nally gene a ed by he Xeon Phi ke nel de ini ion : 1) Func ion o apply o each elemen : ke nel xphi ##name; 2) Func ion ha execu es an enqueued ke nel in pa allel: w appe xphi ##name; 3) Func ion o add a ask o he Con olle queue: name## xphi. compu a ional unc ion, and i s execu ion pa ame e s ( he index space whe e he applica ion will be execu ed, he numbe o ke nel pa ame e s, and he ac ual ke nel pa ame e s). See lines 31 o 35 o Fig. 5. 4.3 Execu ion model: Queue managemen and Ke nel launching As opposi e as he CUDA p og amming model, he o loading MIC cop ocesso p og amming model does no p o ide a queue sys em o manage asynch onous ke nel launchings. We ha e de eloped a FIFO queue sys em o he asynch onous execu ion o se e al ke nel launches on he MIC cop ocesso . When a MIC con olle objec is c ea ed, an asynch onous omp ask is launched. The p og am o his omp ask is checking he possible new ask queue addi ions. When he e is a ask in he queue, he con olle dispa ches/execu es i , and es a s he checking again. The checking is implemen ed using omp locks a oiding hus ac i e wai s. The execu ion o a ask on he MIC is ca ied ou simply by he execu ion o he al eady o loaded pa allel w appe xphi ##name gene a ed unc ion, ha is associa ed wi h he ask ha is being execu ed. The unc ion poin e , and i s execu ion pa ame e s a e de e mined in he ke nel launching. The las ask added is he con olle des uc ion. I s ops he checking, in- ishes he omp ask and, des oys he con olle . 5 Expe imen al s udy We pe o m an expe imen al s udy o e alua e he po en ial ad an ages and cons ain s o he in eg a ion o he MIC cop ocesso in a homogeneous lib a y o CPU-GPU he e ogeneous sys ems. The sec ion consis s o : (1) a desc ip ion o he conside ed s udy cases, (2) a pe o mance s udy o ou p oposal, and (3) a compa ison o he de elopmen e o needed be ween p og amming using he new lib a y ex ension o using de ice endo p og amming models. 5.1 S udy cases We selec ou benchma ks o es he ex ension p oposed in his wo k. Ma ix addi ion The Ma ix addi ion consis s o he sum o wo di e en ma ices, s o ing he esul in a hi d one: C=A+B. Ou MIC implemen a ion o his p oblem is simila o he GPU e sion. Only one gene ic ke nel is de ined by he p og amme . Black-Sholes The Black-Scholes o mula is based on a ma hema ical model o a inancial ma ke . The esul es ima es he p ice o Eu opean-s yle op ions. The p og am, ob ained om he CUDA Toolki Samples, independen ly applies he o mula o he inpu alues o an a ay, calcula ing and s o ing hei esul s. In ou implemen a ion, he same gene ic ke nel de ini ion is used o bo h GPUs and MICs accele a o s.