STATIC MAPPING FOR OPENCL WORKLOADS IN HETEROGENEOUS COMPUTER SYSTEMS

Transcription

1 STATIC MAPPING FOR OPENCL WORKLOADS IN HETEROGENEOUS COMPUTER SYSTEMS 1 HENDRA RAHMAWAN, 2 KUSPRIYANTO, 3 YUDI SATRIA GONDOKARYONO School of Electrcal Engneerng and Inforatcs, Insttut Teknolog Bandung, Jalan Ganesha No.10, Bandung 40132, Indonesa E-al: 1 hrahawan@gal.co, 2 kuspryanto@yahoo.co, 3 ygondokaryono@ste.tb.ac.d ABSTRACT Today, heterogeneous coputer systes (HCS) coonly rely on CPU and GPU, for processng eleents, and OpenCL, for the prograng fraework. In an HCS, a workload should execute on ts best processor to acheve ts best speedup. OpenCL currently entrely lefts the selecton for the best-ft processor, tered as workload appng, to prograers. However, the NP-copleteness of the workload appng task ndcates t s not a trval task to do anually by prograers so that effectve coputatonal approaches are necessary. Ths research proposes a statc appng ethod for OpenCL workloads that autoatcally select the best-ft processor for the workloads. The ethod accepts statc features of a workload and utlzes K-Nearest Neghbor algorth to classfy the workload to ether CPU or GPU. The statc features are collected usng LLVM/Clang copler fraework. To ncrease the accuracy of classfcaton whle keep antanng the physcal eanng of features, the features are reduced usng feature selecton approaches. Two feature selecton odels, flter odel and wrapper odel, are used n ths research. Ths approach was evaluated usng k-fold cross-valdaton aganst 18 OpenCL kernels obtaned fro standard benchark packages. Accordng to the evaluaton results, the workload appng accuracy was n the range of 93% to 97% ndcatng the ethod s well applcable n the HC envronent wth two processors. Floatng-pont operatons and vector-nteger operatons, or floatng-pont operatons and vector-global eory access are the cobnatons of features that a have sgnfcant contrbuton to the classfcaton of workloads. The an contrbuton of the ethod n ths research, copared to prevous related research, les n ts capablty to state features that are sgnfcant n the classfcaton process. Keywords: Heterogeneous Coputng, Workload Mappng, OpenCL, K-Nearest Neghbor, GPU 1. INTRODUCTION Heterogeneous coputng systes (HCS), as a subset of hgh-perforance coputng systes, has been recognzed as a new platfor n coputng and also offers a hgh gan n perforance at a reasonable cost and power consupton [1-3]. Varous coputng workloads n dfferent felds, rangng fro trval atrx-vector ultplcaton progra to ost coplcated bonforatcs progras, share three coon coputng characterstcs: data ntensve, copute ntensve, and control ntensve [3]. Processng devces should be able to accoodate those heterogeneous workload characterstcs, and heterogenety of HCS nherently gves capabltes for processng dfferent types of workloads whch are not well addressed by conventonal hoogeneous coputng systes [1]. HCS s usually bult usng exstng off-the-shelf achnes or processors. Thus, t wll be relatvely low-cost, coply wth ndustry standards, and use proven technology. Basng to Flynn taxonoy for classfyng parallel archtectures [4], HCS can be defned as a synergetc use of processors havng archtectures of SISD (sngle nstructon-strea sngle data-strea), SIMD (sngle nstructonstrea ultple data-strea), MIMD (ultple nstructon-strea ultple data-strea), or MISD (ultple nstructon-strea sngle data-strea). As today t s dffcult to fnd real SISD achnes and no real pleentatons of MISD archtecture, exstng HCS systes ncorporate only SIMD and MIMD archtecture. SIMD achnes are well suted for processng data-ntensve and coputentensve workloads, whereas MIMD achnes would be able to process control-ntensve workloads properly [3, 5]. MIMD archtecture s generally represented by general purpose CPUs. On another spectru of archtectures, graphcs processng unts (GPUs), 4126

2 dgtal sgnal processng (DSPs) processors, and feld prograable gate arrays (FPGAs) are soe devces represent SIMD archtecture. Those devces are also known as coprocessors or accelerators. Wth the rse of general-purpose coputng on GPU (GPGPU), GPU becoes the ost wdely used accelerators n HCS. Fro another pont of vew, HCS could also be defned as the use of coprocessors to ncrease the perforance of coputng nstead of usng only a sngle type of CPU. Accelerator s a ter used wth coprocessor nterchangeably snce a few years back, descrbng ts capabltes to accelerate workloads processng. A CPU wll offload ts workloads to accelerators to accelerate workloads processng. In addton to hardware aspects, software fraeworks are also requred to support HCS n order to be optally used by the prograer. OpenCL s an open standard of the HCS fraework developed by several vendors, such as Intel, AMD, and NVda, cooperatng n Khronos consortu. Accelerators or coprocessors and general purpose CPUs are tered as devces n OpenCL. The support of OpenCL for wde range of devces akes t frequently used n HCS prograng. However, the selecton of devces to execute workloads n OpenCL s left entrely to the prograer. Prograers should be able to atch ther workloads wth avalable devces, consderng characterstcs of workloads and devces, wth the ntenton of achevng good perforance. Inaccurate decsons by runnng workloads on unsutable processng eleents wll brng workloads executon nto suboptal perforance. Ths workload-processor atchng or appng proble s an NP-coplete proble rather than trval one [7]. Thus, not only wrte ther progras, n these crcustances, prograers also have to handle ths non-trval addtonal tasks. Unfortunately, exstng software fraeworks for HCS have less features for assstng prograers to deterne the ost sutable processng eleent for ther workloads. Due to ths appng proble nherts NP-coplete proble, an approach that can be used to overcoe the proble s by predctng the best-ft processor for a gven workload. Classfcaton algorths could be used to accoplsh ths task. Ths paper ntroduces an approach to ap workloads to processors by usng K-Nearest Neghbors (KNN) as the classfcaton algorth. The purpose of the approach s to select the best processor, ether CPU or GPU, by predcton. In ths research, KNN algorth s used n conjuncton wth statc proflng to collect the characterstc of workloads, and feature-selecton algorths to select hgh-correlated features. Ths paper s organzed as follows. Secton 2 provdes otvatons for ths research. Soe related works n workload appng are explaned n Secton 3. Secton 4 covers soe fundaental concepts about OpenCL and KNN algorth. Proposed ethodology n ths research s descrbed n secton 5. Results of experents n ths research are presented and dscussed n secton 6. Secton 7 suarzes the contrbutons of ths research. Conclusons and possble future works of ths research are elaborated n secton MOTIVATION Ths research s otvated by soe fndngs that dfferent appng of workloads onto processors could brng nto sgnfcantly dfferent perforance of workloads executon on HCS. In ths secton, sx OpenCL kernels are exaned by executng the on two dfferent types of processor, naely CPU, and GPU. Fgure 1 depcts the speedups of sx OpenCL kernels, each of whch s executed on CPU, and GPU. Fgure 1: The Speedup over Sngle-Core CPU of Sx OpenCL Kernels Runnng on Mult-Core CPU and GPU. As depcted n Fgure 1, there are two groups of workload that deonstrate dfferent behavor regardng speedups they acheve both on CPU as well as GPU. The frst group conssts of FFT 1D, local-eory atrx ultplcaton, and box flter vertcal. The second group conssts of hstogra, uncoalesced atrx-vector ultplcaton, and bnary search. The frst group acheve the best perforance when they run on CPU, whereas the last one acheve the best perforance when they run on GPU. If the frst group s assued to run on GPU, then t wll lose, on average, about three-fourth of ther best speedup. On the other hand, f the second group run otherwse on GPU, t wll lead to, on average, two tes slower than ts best perforance. 4127

3 Therefore, ths clearly shows that dfferent workload needs dfferent appng n order to acheve ther best perforance. If only a sngle appng rule appled, for nstance always run workloads on GPU or always run workloads on CPU, then, as prevously descrbed, t s probably wll end up ether n lower speed-up, or turn nto slow-down. 3. RELATED WORKS Soe prevous works were conducted to allevate the proble of deternng processors to execute workloads. Early research n the feld of heterogeneous coputng, lke n [8], used nterconnected sngle-core processors wth dfferent speed as ther odel. Soe of the also studed appng probles n sulaton envronents where the perforance of all tasks on all processors s assued known n advance. Braun et al. [9] was coparng 11 heurstcs used for statc appng of tasks on xed-achne heterogeneous systes. Those heurstcs were appled wth estated executon te (ETC) atrces to fnd the best heurstc for ndependenttasks appng on heterogeneous systes. A row n an ETC contans the estated executon tes for a gven task n soe achnes, whch also represents achne heterogenety. The research concluded that genetc algorth (GA) s the best heurstc n the heterogeneous appng sulaton. Other research n statc task atchng for heterogeneous systes also addressed dependenttask odel, whch s odeled usng drected acyclc graph (DAG), lke n [10] that also pleents GA as ts heurstc. Wrensng and Greg [11] proposed an approach called elastc coputng that ntroduced ultpleentaton of workloads on varous processor types, whch was cobned wth lnear-regresson generated fro workload profles, n order to fnd the best processor type to execute workloads n queston. Sandreser et al. [12] ntated an external specfcaton, called platfor descrpton language (PDL), as an abstracton for ultple dfferent workloads pleentatons and ultple dfferent hardware platfors, as the bass for the proposed approach for fndng the ost sutable processor to execute the workloads. Approaches n [11,12] share the coon property,.e. provdng ultple dfferent workload pleentatons s andatory, whch s ltng the to be wdely appled. Another ethodology n workload appng, proposed by Albyarak et al. [13], usng three dfferent an algorths,.e. greedy algorth (GA), proved verson of greedy-algorth (IA), and xed-nteger prograng (MIP), whch all of the requred nput n the for of characterstcs of kernels (workload). In ths approach, the characterzaton of a kernel s accoplshed by executng each kernel on each processors. Ths characterzaton technque s known as dynac proflng, whch has relatvely hgher cost than ts statc counterpart. Grewe and O Boyle [6] and Tarakj et al. [7] ntroduced the use of achne learnng to decde the ost proper processor to execute a workload. Both of the used support vector achne (SVM), appled statc-proflng to collect features fro workloads, and used prncpal coponent analyss (PCA) to reduce the feature sze. If t s necessary to know what features affectng appng decsons, then the use of PCA cannot answer the queston as t wll transfor features nto new feature space. 4. FUNDAMENTAL CONCEPTS Soe basc theores about the OpenCL prograng fraework, and the KNN classfcaton algorth, eployed n ths research, wll be elaborated brefly n ths secton The OpenCL Prograng Fraework OpenCL standard specfes four odels n the prograng fraework, naely platfor odel, executon odel, eory odel, and prograng odel. The platfor odel presents herarchcal organzaton abstractng the hardware n OpenCL. A platfor conssts of a host connected to one or ore OpenCL devces. An OpenCL devce s consttuted by one or ore copute unts (CUs), whch are further dvded nto one or ore processng eleents (PEs) [14]. The platfor odel also specfes that there s one processor coordnatng executon (the host) and one or ore processors capable of executng OpenCL C code (the devces) [3]. The executon odel defnes two ters for two dfferent unts of executon, naely kernels that run on one or ore OpenCL devces, and a host progra that runs on the host processor. The kernel ter n OpenCL refers to where the an task, whch s usually assocated wth coputaton, s placed. At run-te, kernels would be n the for of work-tes that executes n groups, tered as workgroups. Relatng to the platfor odel, these work-tes would be run on OpenCL devces that are deterned by the host progra. A context s 4128

4 used to anage envronent wthn whch kernels run. A coand-queue assocated wth a certan devce s also set up n the context. To run a kernel on a certan devce, a coand s subtted to coand-queue, n the for of N-densonal ndex space, tered as NDRange. In a devce, work-tes n a workgroup would be further grouped nto wavefronts that are executed n lockstep n a copute-unt [15]. The eory odel defnes organzatonal aspects of eory that control the nteracton of values n eory as they are accessed fro unts of executon. It would help prograers to verfy the correctness of ther progra. The odel classfes eory nto host eory and devce eory, based on ts drect-avalablty n a devce or n a host. Ths odel also specfes four regons of eory n an OpenCL devce, naely global eory and constant eory that have scope n the devce, local eory that s only accessble by work-tes n the sae workgroup, and prvate eory that s vald only for a work-te. Besdes the specfcaton of eory regons, the eory odel of OpenCL also specfes about eory objects, shared vrtual eory, and consstency odel. The last odel n OpenCL s the prograng odel. It defnes how the concurrency odel s apped to physcal hardware. It abstracts a host processor and one or ore OpenCL devces as a sngle heterogeneous parallel coputer syste. Ths odel deternes that an pleentaton of the OpenCL fraework should consst of OpenCL platfor layer, OpenCL runte, and OpenCL copler K-Nearest Neghbor (KNN) Classfcaton In the feld of data classfcaton, KNN algorth falls nto statstcal learnng class whch ts classfcaton process s based on nstances of data [16]. Ths algorth s a lazy-learnng algorth because t delays the nducton or generalzaton process untl classfcaton occurs. KNN s usng the prncple that nstances n a dataset wth slar propertes are n general closer each other than wth the others wth dfferent propertes. In a supervsedclassfcaton case, whch s data has a label assocated wth each of ts nstances, to fnd a label for an unclassfed nstance, KNN algorth wll dscover k nearest neghbors and assgn the sngle ajorty label to the nstance. The general steps n KNN s descrbed n Fgure 2. Let x be an nstance to be classfed Read tranng data Y Set k: the nuber of nearest neghbors to fnd Defne dstance functon D(x,y) Evaluate D(x,Y): dstance fro x to each te of Y Sort D(x,Y) n ascendng order Get frst k tes fro D(x,Y), assgn the to Yk Deterne the ajorty label fro Yk, and use as label for x Fgure 2: Steps to Classfy an Instance of Data Usng KNN. In the context of supervsed classfcaton, nstances can be consdered as ponts n an n- densonal space. Proxty or dstance between nstances hence can be easured usng soe dstance-functons [16] as presented n Table 1. Table 1: Soe Functons to Measure Dstance. Dstance Functon 1 2 Eucldan: 2 D ( x, y) x y 1 Mnkowsk: r D( x, y) x y 1 Manhattan: D( x, y) 1 x y Chebychev: D( x, y) ax x Caberra: D( x, y) 1 1 y x y x y 5. METHODOLOGY In ths research, there are two an processes carred out to ap a workload to two type of processors, naely a GPU and a CPU. Those processes are workload characterzaton and workload appng, wth the sequence of the s depcted n Fgure 3. Workload Workload Characterzaton Character of Workload Workload Mappng 1 r Map of Workload Prosesor Fgure 3: The sequence of stages n the proposed workload appng approach. The workload characterzaton process takes a workload n the for of OpenCL kernel as ts nput, deternes the workload characters, and ssues the character of the workload as the output. The 4129

5 character of a workload then passed to the workload-appng process that wll predct the ost sutable processor for the workload. The output of the latest process s a par of workloadprocessor that represents the processor n whch the workload to run. In ths research, the nuber of nvolved workloads and processors n a sngle appng step are assued to be one workload and two processors wth dstnct types. Ths assupton s taken to observe the perforance of ths ethodology n a sple appng case. Further developent and generalzaton for ore coplex appng scenaros could be done based on the result of ths research Workload Characterzaton In ths research, a characterstc of a workload s a collecton of features related to a workload. To obtan values of these features, ths research uses both statc and dynac proflng technques. The statc proflng approach s responsble for coputng the frequency of occurrence of operatons n a workload, whereas the dynac proflng s responsble for collectng soe dynac aspects of workloads lke nput sze and eory-transfer sze. Canddates of statc features coe fro three types of operaton: coputaton, eory, and control. As no support for I/O operaton n GPU, the I/O operaton type s not nvolved n ths research. The ratonale to nvolve those types of operatons are all coputatons are coonly coposed of the. Ths research uses nput sze and eory sze (n bytes) for dynac features for the reason they wll deterne the total sze of nstructons. The workload features to use n ths research are lsted n Table 2. The statc proflng approach uses an analyss-pass based on Clang/LLVM copler fraework. Ths analyss pass reads an OpenCL kernel that has been copled nto LLVM nteredate-representaton (IR) and coputes the frequency of certan operatons n the IR. In ths research, the operatons are classfed nto ordnary operaton and coplex operaton. The frequency of occurrence for ordnary operatons, lke scalar nteger operatons, floatng-pont operatons, etc., can be coputed usng a sple algorth. The algorth uses a counter varable for each operaton under observaton. The algorth wll ncrease the counter varables when t encounters the assocated operatons. Table 2: Features of A Workload Acqured Usng Statc and Dynac Proflng Technques. Operaton Type No. Feature Nae Feature Type Arthetc 1 Scalar Integer Operatons Statc 2 Floatng-pont Operatons 3 Vector Integer Operatons 4 Vector Floatng-pont Operatons Meory 5 Scalar Local Meory Access 6 Vector Local Meory Access 7 Scalar Global Meory Access 8 Vector Global Meory Access 9 Coalesced Meory Access Control 10 Barrers 11 Condtonal Branch 12 Dvergent Branch 13 Condtonal Branch Cost 14 Uncondtonal Branch N/A 15 Input Sze Dynac 16 Meory Sze A coplex operaton s an operaton wth specal eanng. The coplex operatons are coalescedeory-access operatons and dvergent-branch operatons. A coalesced-eory-access operaton allows adjacent threads to concurrently access values resdng n adjacent eory locatons. A coalesced-eory-access operaton s actually an ordnary eory-access operaton, but soe specal treatents take place when a processor executes t. One of the ndcatons whether or not a eory access s a coalesced one s whether or not the eory access uses thread-ndex or workgroup-ndex. A dvergent-branch nstructon s a condtonal-branch nstructon whch akes threads follow a dfferent path of executons. A condtonal branch s possbly dvergent when t ncludes thread-ndex or workgroup-ndex n ts condtonal part. In ths stuaton, to deterne whether or not a eory operaton s a coalesced-eory nstructon and whether or not a condtonal-branch nstructon s a dvergent-branch nstructon, soe addtonal coputatons should be carred out. The coputatons consder the ndcaton that both operatons use thread-ndex or workgroup-ndex. The flow of workload characterzaton s started wth coplng an OpenCL source fle nto an IR and storng t n a text fle. The next step s runnng the analyss pass aganst the text fle contanng the IR. The analyss-pass could be run usng the opt tool n the LLVM fraework. The analyss-pass runs the workload characterzaton process usng the algorth descrbed before. The output of the analyss-pass s a fle contanng the values of the features of the workload. 4130

6 5.2. Workload Mappng The objectve of ths step s to decde whch processor to run a workload. Ths task s accoplshed usng the KNN classfcaton algorth. The nput for ths step s a fle contanng features of workloads. Ths step conssts of three sub-steps, naely noralzaton, feature selecton, and classfcaton Noralzaton Due to the lted nuber of avalable workloads, and the need of a large enough saple sze for data classfcaton, to ncrease the saple sze of the workload, all of the workloads are run wth vared nput sze. Ths strategy gves dentcal feature values for groups of workloads, except for the nput sze feature and the eory access sze feature. To dstngush between two sets of feature values fro the sae workload wth dfferent nput szes, the total nuber of operatons n the workload can be used. By ultplyng sze of operatons n workload code wth the nuber of work-tes, the total nuber of operatons n the workload executon could be estated. To nfor the classfcaton algorth about the relatonshp of sets of feature values that coe fro the sae progra but wth dfferent nput sze, the total nuber of operatons s then dvded by the eory access sze. Ths sequence of coputatons s called noralzaton, ntroduced by Grewe & O Boyle [6], and forulated n Eq. (1). nuber of worktes noralzed value operatons sze (1) data transfer sze Feature Selecton To ncrease the perforance of KNN classfcaton, a sequence of steps, called feature selecton, s carred out to select only portant features of OpenCL kernels. The portant features have hgh relevance to the class label assgnent n the workload data. In ths case, the portant features are very decsve whether a workload s better to run on CPU or GPU. The feature selecton only run once as the ntalzaton process of the classfer, and t does not run n the classfcaton step. Ths research uses two feature selecton odels. The frst odel s flter odel, whch s ndependent of the classfcaton algorth. The second one s wrapper odel, whch uses a classfcaton algorth to produce selected features. The sequence of flter odel and wrapper odel s depcted n Fgure 4. Feature Set Flter Model Feature Subset Wrapper Model Feature Subset Fgure 4: The Sequence of Flter Model and Wrapper Model n Feature Selecton Process. Flter odel wll copute ranks for each feature of OpenCL workloads. A dataset contanng records of OpenCL kernels wth ther feature values s the nput for ths process. In ths research, flter odel uses two etrcs, Pearson Correlaton Rank [17] and Fsher Score [18], as the rankngs for features. The ratonale for the use of both etrcs s to gve better confdence about the order of the features produced by the flter odel. In ths research, the flter odel s also set to feed the wrapper odel, the next step of feature selecton, wth reduced feature sze that wll decrease coputng cost of t. Features that have been ranked by flter odel are then pruned by selectng best features. The best features can be selected by settng a threshold ether objectvely or subjectvely. Eq. (2) and Eq. (3) forulate Pearson Correlaton Rank and Fsher Score respectvely. R( ) F( x ) j ( k ( x k k, c n k k ( 1 c nk x 1 k, x )( y 2 x ) 1 k1 k1 j k ) ( j j 2 k ) 2 k y) ( y k y) Wrapper odel wll further select the features produced by flter odel, by evaluatng the aganst a classfcaton algorth. The ratonale for usng wrapper odel s to select features that ft wth the classfer n the classfcaton phase. In ths research, wrapper odel eploys KNN as ts classfcaton algorth. Wrapper odel works teratvely, whch n each teraton the wrapper odel wll select a feature subset, evaluate t, and decde whether to contnue to the next teraton or to ternate the teraton based on a stoppng condton. The stoppng condton used n ths research s f the entre feature-subsets have been evaluated. The feature-subset wth the best accuracy n KNN classfcaton then wll be selected for the output of wrapper odel. Fgure 5 depcts the flow of wrapper odel for feature selecton. 2 (2) (3) 4131

7 Subset selecton Feature set No Perforance evaluaton aganst a classfcaton algorth Meet the stoppng condton? Yes Select the best subset Fnal feature subset Fgure 5: The Flow of Feature Selecton wth Wrapper Model Classfer Evaluaton The standard cross-valdaton technque s used to evaluate the perforance of KNN classfcaton for workload appng. Aong varants of crossvaldaton technque, ths research uses n-fold cross-valdaton technque. In the n-fold crossvaldaton, the data s randozed and parttoned nto n equally szed subsets. In an teratve way, n the th teraton, the th subset s selected as the test set and the rest n-1 subsets are erged to for the tranng set. In each teraton, the perforance of KNN classfer s easured, usually usng loss or accuracy as the etrc, and all of the perforance values are averaged at the end of entre teratons. It s portant to note that none of the data n the test set s used for tranng the odel Experental Setup In ths research, experents were conducted wth the objectve to evaluate the perforance of workload appng usng KNN classfcaton. All experents were carred out on a heterogeneous coputer wth hardware confguraton descrbed n Table 3. Table 3: Hardware Confguraton for All Experents. CPU GPU Type Intel Core k NVIDIA Tesla C2075 Core clock 3.4 GHz 1.15 GHz Nuber of 4 (8 wth 448 cores HyperThreadng) Meory szes 8 GB 6 GB Meory 21 GB/s 144 GB/s transfer speed OpenCL platfor Intel OpenCL NVIDIA CUDA The workload saples were selected fro exstng software packages: AMD APP SDK and CUDA SDK. There were 18 OpenCL kernels that each of the executed wth four dfferent nputs, producng 72 kernel-executons. Each of 72 kernel confguratons was executed 20 tes on CPU and GPU, resultng averaged workload executon tes on CPU and GPU. Ether CPU or GPU, whch gave the best perforance n executng a workload, was set as the label for the workload n the data set. The statc feature values of the kernel are also collected to for the dataset along wth the predefned label. The K n the KNN classfer was set to 1 and 3. These values were based on ntal experents that evaluated the K values of 1, 3, 5, 7, and 9 and showed that the best KNN perforance, n the workload appng case, s gven by K-values of 1 and 3. To evaluate the KNN classfer perforance durng feature selecton process usng wrapper odel, n-fold cross-valdaton was used wth n was set to 10. The fnal assessent of the KNN classfer for workload appng also utlzed n-fold cross-valdaton wth the n was set to 4 and 6, consderng the aount of test data and tranng data. In addton to the orgnal order of the data set, perutatons were also appled to the data set. Ths was done to observe the effect of perutatons as the consequence of the reuse of workloads to construct the data set. 6. RESULTS AND DISCUSSION Ths secton shows and dscusses the results of feature selecton process and fnal assessent of the workload appng process Feature Selecton It has been found fro experents, f the entre 14 features were used for classfcaton usng KNN, the average accuracy was 88.96%. The accuracy could be ncreased by applyng feature selecton process. As entoned n prevous chapter, two odels of feature selecton were nvolved, flter odel and wrapper odel. The results of flter odel that coputed Pearson coeffcent and Fsher score for the 14 features are depcted n Fgure 6. Fro the results, by specfyng the threshold for Pearson coeffcent and Fsher score by 0.2 and 0.1 Fgure 6: Pearson Coeffcent and Fsher Score for 14 Intal Features. 4132

8 respectvely, t was obtaned sx selected features as lsted n Table 4. These thresholds were set subjectvely by observng the values and estatng the nuber of features should be selected. Table 4 also ndcates the nuberng of the features for the purpose of referencng. The selected features were further selected by the wrapper odel that used KNN classfer wth K=1 and K=3. Fgure 7 and Fgure 8 descrbe the perforance of KNN classfcaton n wrapper odel aganst varyng features and feature szes. Fgure 7 and Fgure 8 were obtaned fro KNN classfer wth K=1 and K=2 respectvely. For each subset of each possble subset sze fro the entre avalable feature subsets, only two best cobnatons of features are presented n both fgures. Fro the fgures, t s clear that the use of two features, ether floatngpont operatons and vector nteger operatons (feature 4 and 5), or floatng-pont operatons and vector global eory access (feature 4 and 6), n KNN classfer gve best perforances wth the accuracy s ore than 96%. These facts could also be a recoendaton for prograers to notce these three deternstc features n ther progra: floatng-pont operatons, vector nteger operatons, and vector global eory access. Table 4: Features selected usng flter ethod. Feature Feature Nae Nuber 1 Branch Condtonal 2 Branch Max Dvergence Cost 3 Branch Uncondtonal 4 Floatng-pont Operatons 5 Vector Integer Operatons 6 Vector Global Meory Access 6.2. Workload Mappng The results of experents that evaluate the KNN classfer that perfored workload appng task are suarzed n Table 5. The results show that the accuracy s rangng fro 93.1% to 100%. The lowest accuracy was obtaned when perutaton was not appled to the dataset. It was occurred due to data tes obtaned fro the sae workload were n contguous locaton. Ths crcustance ade selected testng dataset possbly had no representatve dataset n the tranng dataset, therefore sclassfcatons were lkely occurred. The hghest accuracy was acheved wth the peruted dataset. The perutaton dstrbuted data tes to the testng dataset and the tranng dataset. The dstrbuton would ake data tes coe fro each workload ore probable to resde n the testng dataset and the tranng dataset. Hence, evaluaton wth these datasets ended up wth accurate classfcaton. Fgure 7: Perforance of KNN Classfer n Wrapper Model wth K=3. Fgure 8: Perforance of KNN Classfer n Wrapper Model wth K=3. Table 5: Suary of Evaluaton of the KNN Classfer Testng Scenaros Manually Splttng of Tranng-Testng Dataset K-Fold Cross Valdaton Data Perutaton Yes No Yes No Testng Paraeters Testng Dataset Sze Tranng Dataset Sze K n KNN Accuracy (%) K(K-Fold Cross Valdaton) The results of experents usng the testng dataset wth the sze of 12 and 18, wthout perutaton, are presented n Table 6 and Table 7. Noralzed speedup s used to copare speedups of each kernel when t was executed on CPU, GPU, and the processor that was selected by the KNN classfer aganst ts best speedup. KNN classfcaton that used both of testng datasets faled to classfy HorzontalSAT0 kernel wth the nput sze of However, although the KNN classfer faled to classfy the kernel, the perforance of the sclassfed kernel s so close to the optal one. When the kernel was executed on the processor that was selected by the KNN classfer, t ganed the speedup of relatve to 4133

9 ts best speedup. Ths result of experents ndcates that the KNN classfer can be used to deterne the ost sutable processor to execute a kernel wth hgh classfcaton accuracy. Table 6: Perforance Acheved by 12 Kernels When They Were Mapped Usng Always-CPU, Always-GPU, and KNN-Classfer. No. Kernel Noralzed Speedup CPU GPU Mappng 1 DCT_2048_ MatrxMultplcaton_ MatrxMultplcatonLocal_ OclMatVecMulCoalesced1_ OclMatVecMulCoalesced2_ HorzontalSAT0_ VertcalSAT_ HorzontalSAT0_ box_flter_vertcal_ box_flter_horzontal_ hstogra_256_ hstogra_256_ Table 7: Perforance Acheved by 18 Kernels When They Were Mapped Usng Always-CPU, Always-GPU, and KNN-Classfer. No. Kernel Noralzed Speedup CPU GPU Mappng 1 DCT_2048_ BnarySearch_ MatrxMultplcaton_ MatrxMultplcatonLocal_ OclMatVecMulUncoalesced0_ OclMatVecMulCoalesced0_ OclMatVecMulCoalesced1_ OclMatVecMulCoalesced1_ OclMatVecMulCoalesced2_ HorzontalSAT0_ VertcalSAT_ HorzontalSAT0_ box_flter_vertcal_ box_flter_horzontal_ hstogra_256_ hstogra_256_ hstogra_256_ AESEncrypt_ SUMMARY OF CONTRIBUTIONS In contrast to the related works [6] and [7] explaned n Chapter 3, ths research proposes a appng approach that can descrbe the contrbuton of features of workloads n the appng process, whch s accoplshed through the use of feature selecton. The use of KNN classfer akes the appng approach potentally ore scalable as the KNN algorth s nherently a ultclass classfer. 8. CONCLUSION AND FUTURE WORKS The heterogenety of OpenCL workloads and processors n heterogeneous coputer systes akes workload appng task to select the ost sutable processor for a certan workload very crucal for the perforance of the workload executon. The contrbuton of ths research s a new soluton for the workload appng proble by utlzng KNN algorth that was proved usng feature selecton. Ths approach s able to help prograers to deterne processors to execute ther workloads optally, whch s not a trval task to perfor anually. Ths appng approach used statc features of workloads for the classfcaton process. There were ntally 14 statc features whch were reduced to a lower sze usng feature selecton process. The feature selecton n ths approach eployed two odels, naely flter odel and wrapper odel. These odels were nvoked n sequence and have refned the accuracy of the KNN classfcaton up to 12%. The cobned features of floatng-pont operatons and vector nteger operatons, or floatng-pont operatons and vector global eory access are eprcally gan the hghest accuracy n the workload-processor appng. The experents that were conducted usng 18 workloads, pcked fro standard benchark packages, have shown the accuracy of classfcaton rangng fro 93.1% to 100%. Ths range of value ndcates lower and upper bound for the accuracy of the workload appng approach. Ths range of value also suggests that the KNN classfer accurately ap workloads to two heterogeneous processors. Although ths result was obtaned fro appng wth two processors target, t s possble to ncrease the nuber of the processor to ore than two processors. Ths work has not yet addressed the proble of schedulng a set of workloads nto a set of processors. Thus ths schedulng case s left for the future works. There are also soe open probles related to ths feld of research. One to consder s about nvolvng other perforance paraeters n the appng approach such as power consuptons, and load balancng. Another appng schee, lke dynac appng, also has possble advantage over current approach, so t s worth to explore. The proposed appng approach s also possble to extend to handle ult-kernel schedulng proble. REFERENCES: [1] Khokhar A.A., Prasanna V.K., Shaaban, M.E., & Wang, C.L., Heterogeneous coputng: Challenges and opportuntes, Coputer 26(6), June 1993, pp [2] Kuar R., Tullsen D.M., Joupp N.P., & Ranganathan P., Heterogeneous chp 4134

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