25 2 ) 15 (W 力電 idle FMA(1) FMA(N) 実行コード Memcopy matmul 1 N occupancy gridsize N=256 Memcopy blocksize 288x288 (matmu
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1 GPU 1, 2 1, 2 1, 2 1, 2 1, 2, 3 GPU NVIDIA GeForce GTX285 Tesla S17 1 GPU GPU GPU 2W CPU GPU GPU GPU GPGPU 92.8% GPU GPU Correlative Analysis of Performance Counters and Power Consumption on GPUs Hitoshi Nagasaka, 1, 2 Naoya Maruyama, 1, 2 Akira Nukada, 1, 2 Toshio Endo 1, 2 and Satoshi Matsuoka 1, 2, 3 1. GPU (GPGPU) 1) TSUBAME GPU HPC 2) GPU GPU 1W 2 GPU % GPUs are being employed in large-scale supercomputing environments, where their power consumption is a first-class design constraint. To reduce their power consumption, we propose a prediction model that leverages application behavior observable through performance counters. It predicts the power consumption of a given GPU kernel by a liner regression that uses the performance counter values when the kernel is executed, such as instruction throughput, register usage, memory accesses, and number of branches. Our experimental studies show that the model achieves up to 92.8% accuracy. We also found that, among others, instruction throughput and memory accesses are the most positively correlated with power, while number of executed branches is the most negatively correlated one. 2. GPU GPU 1 FMA fma GPU c 29 Information Processing Society of Japan
2 25 2 ) 15 (W 力電 idle FMA(1) FMA(N) 実行コード Memcopy matmul 1 N occupancy gridsize N=256 Memcopy blocksize 288x288 (matmul) dynsmemperblock stasmemperblock registerperthread idle idle gld coherent 2 4 gst coherent branch divergent branch divergent branch 2 instructions warp serialize GPU : GPU GeForce GTX BIOS PCIExpress : GPU 12V 3.3V % 3.2 CUDA CUDA Profiler 3) 1 occupancy CUDA ( ) GPU SM SM SM 2 c 29 Information Processing Society of Japan
3 情報処理学会研究報告 てブロック数が SM 数未満であった実行は解析対象から除外した の通りである また 使用したマシンの OS は OpenSUSE11.(kernel: pae) 3.3 相関性の解析 CPU は AMD Phenom(tm) 95 Quad-Core Processor(2.2GHz) である CUDA ドライ 平均消費電力を目的関数 単位時間あたりのカウンタ値を説明変数として線形回帰分析に バ 2.2 NVIDIA ドライバ を用いた かけ消費電力を予測する すなわち 消費電力を P カウンタの種類を n パフォーマンス 図 2 に実験環境の全体図を示す カウンタ値を p として P = c + n X ci p i 12V電源 A/Dコンバータ (1) i=1 と表せる 最も P を高い精度で予測できる ci を求める また その精度を解析するために leave-one-out 手法を用いる 具体的にはまずサンプル i を排除し残りのサンプルで回帰分 析を行い その結果からサンプル i の消費電力の予測精度を調べ この操作を全サンプルに 対して行う この時 カウンタ値は実行時間に依存するもの (命令数等) は単位時間あたり ＧＰＵ ライザーカード(右図参照) とし さらに種類によりサイズ等が異なる為標準化 (平均, 分散 1) した後に回帰分析にか ける また どのカウンタ値が消費電力との相関が強いかを調査する為 回帰係数の比較を 行う 図 3 ライザーカード 図 2 マシン全体図 4. 準備 実験 GPU における消費電力を測定するには ATX 電源の 12V 線から供給される電力 PCIe 4.1 実 験 環 境 から供給される電力の 2 箇所を測定する必要がある 12V 線での測定は図 2 に示すように 電流センサを装着するだけで可能となる 一方 PCIe から供給される電力は 3 に示すよう 表 2 GeForce GTX 285 の詳細 Total amount of global memory 1Gbyte Number of multiprocessors 3 Number of cores 24 Total amount of constant memory 64Kbyte Total amount of shared memory per block 16Kbyte Total number of registers available per block Warp size 32 Maximum number of threads per block 512 Maximum sizes of each dimension of a block 512x512x64 Maximum sizes of each dimension of a grid 65535x65535x1 Maximum memory pitch 256Kbyte Texture aligment 256byte Clock rate 1.48GHz にライザーカードをはさみ さらにその中から 12V 3.3V の電力を供給している配線を測 定する必要がある 電流計には株式会社シナジェテック製 ST-36 を用いる これは 計測の際に配線に加 工を必要としないクランプセンサを用いている また 電流計と GPU コードのカーネル関 数のタイムスタンプの差異を最小限に抑えるために同一のマシンに接続している サンプリ ング間隔は 1ms とした 4.2 計 測 実験に使用したコードは CUDA SDK 付属のサンプルコードである カーネル関数呼び 出しの前後でタイムスタンプを取得し 後に電流計測の時間と照らしあわせて電力を算出す る これらの元のコードではカーネル関数の実行時間が非常に短いものが多いため 計測の 誤差を小さくする為カーネル内処理を繰り返し実行するように変更し すべてカーネルの 今回用いた GPU は NVIDIA 社製 GeForceGTX285 でありアーキテクチャの詳細は以下 個々の実行時間が 1 秒間となるようにした 3 c 29 Information Processing Society of Japan
4 leave-one-out.6 7.2% 数係.2 4 warp serialize 帰回 2.8 h -.2 b ranc b l o ck S i z e r anch e ri a l e iz o c k e nt_b e rbl r Bl o c k g ri d S iz e a ncy a d n t n t -.4 d i v erg w arp_s m emp e mpe o ccup r Thre s t erpe g l d _c o here o here c ti o n s g st_c in stru d yns s tasm r egi -.6 カウンタ 比 サンプル 5.2 GPGPU GPU DVFS 4) (DVFS) 5 instructions 24.9% Maury branch 5) GPU 4 c 29 Information Processing Society of Japan
5 OpenMP ULP-HPC: Microsoft Technical Computing Initiative HPC-GPGPU: Large-Scale Commodity 17% 26% Accelerated Clusters and its Application to Advanced Structural Proteomics ) SamuelS. Stone, JustinP. Haldar, StephanieC. Tsao, Wen-MeiW. Hwu, Zhi-Pei Liang, and BradleyP. Sutton. Accelerating advanced mri reconstructions on gpus. GPU In CF 8: Proceedings of the 28 conference on Computing frontiers, pp , 28. 2). tsubame., Vol.5, 7% No.2, pp. 1 16, 29. 3) NVIDIA. Cuda profier, 29. 4),,,,. DVFS., No.8, pp , ) Matthew C. Maury, F. Blagojevic, C. D. Antonopoulos, and D. S. Nikolopoulos. Prediction-based power-performance adaptation of multithreaded scientific codes. Parallel and Distributed Systems, IEEE Transactions on, Vol.19, No.1, pp , 28. 9% 6) Sara Baghsorkhi and Wen mei Hwu. Analytical performance prediction for evaluation and tuning of GPGPU applications. In Workshop on Exploiting Parallelism using GPUs and other Hardware-Assisted Methods (EPHAM 9), In conjunction with The International Symposium on Code Generation and Optimization (CGO) 29, 29. ED Baghsorkhi GPU 6) GPU Da-Qi Ren GPU FMA 5 c 29 Information Processing Society of Japan
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1,a) 2,b) 3 Modeling of Agitation Method in Automatic Mahjong Table using Multi-Agent Simulation Hiroyasu Ide 1,a) Takashi Okuda 2,b) Abstract: Automatic mahjong table refers to mahjong table which automatically
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1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: matsunagah@murase.m.is.nagoya-u.ac.jp, {ide,murase,hirayama}@is.nagoya-u.ac.jp,
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1 1 1, Extraction of Transmitted Light using Parallel High-frequency Illumination Kenichiro Tanaka 1 Yasuhiro Mukaigawa 1 Yasushi Yagi 1 Abstract: We propose a new sharpening method of transmitted scene
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Vol.55 No.1 2 15 (Jan. 2014) 1,a) 2,3,b) 4,3,c) 3,d) 2013 3 18, 2013 10 9 saccess 1 1 saccess saccess Design and Implementation of an Online Tool for Database Education Hiroyuki Nagataki 1,a) Yoshiaki
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