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Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
A Comparison of GPU Executi...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Timeline
1 Introduction and...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
BSP-based model Vs. Machine...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Games and Video Cards
80’ -...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Graphic Processing Units - ...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
General Purpose GPU - GPGPU...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
GPU Versus CPU
Nowadays GPU...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
CUDA, GPUs and Memory space...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
RoadMap of architectures of...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
RoadMap of architectures of...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Compute Unified Device Archi...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
GPU Programming Model
A GPU...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Top 500 Supercomputers
Inte...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Top 500 Green Supercomputer...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
BSP-based model Vs. Machine...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Amdahl’s law and Flynn’s Ta...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Parallel Random Access Mach...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Bulk Synchronous Parallel M...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Bulk Synchronous Parallel M...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Analytical Model Published
...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
BSP-based model Vs. Machine...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Machine Learning Techniques...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Linear Regression (LR)
It a...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Support Vector Machines (SV...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Support Vector Machines (SV...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Random Forest (RF)
Random F...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
BSP-based model Vs. Machine...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
GPUs of the Testbed
Model C...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Algorithm Testbed
9 differen...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Dataset
10 Times each sampl...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Features of the Machine Lea...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Use Cases of the Analytical...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Log transformation
We first ...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Results Machine Learning - ...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Results Machine Learning VS...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Results Machine Learning VS...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Conclusions
Fair comparison...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Future Works
Irregular benc...
Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison
Thanks for your attention
R...
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SlidesA Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

  1. 1. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling Ph.D(c) CS Marcos Amar´ıs Gonz´alez Advisor: Dr. Alfredo Goldman vel Lejbman Co-advisor: Dr. Raphael Yokoingawa de Camargo December, 2016 (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 1 / 32
  2. 2. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Timeline 1 Introduction and Motivation 2 Parallel Programming Models BSP-based Analytical Model for GPUs 3 Machine Learning Techniques 4 Comparison Methodology Results Conclusions and Future Works (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 1 / 32
  3. 3. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison BSP-based model Vs. Machine Learning 1 Introduction and Motivation 2 Parallel Programming Models 3 Machine Learning Techniques 4 Comparison (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 1 / 32
  4. 4. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Games and Video Cards 80’ - First video driver Evolution of the games 3D. It is nec- essary to apply textures, lights, shad- ows, reflections, etc. It was also necessary more computing power For this, the video cards became to be more flexible and powerful (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 2 / 32
  5. 5. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Graphic Processing Units - GPUs The term GPU was popularized by Nvidia in 1999, who invented a GeForce 256 like the first GPU in the world. In 2002 the first General Purpose GPU was launched. The term GPGPU was created by Mark Harris. The main manufacturer of GPUs are NVIDIA and AMD. In 2005 NVIDIA launched CUDA. Deep Learning, Virtual Reality. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 3 / 32
  6. 6. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison General Purpose GPU - GPGPU Main program execute in the CPU (host) and it is responsible to start the execution in the GPU (device). These GPUs have their own hierarchy of memory and data must be transfered through the PCI Express. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 4 / 32
  7. 7. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison GPU Versus CPU Nowadays GPUs are capable to perform much more efficient computing operations than CPUs multicores. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 5 / 32
  8. 8. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison CUDA, GPUs and Memory spaces A GPU has many processors P, all processors have the same clock rate R and they are divided in Multiprocessors. A CUDA Kernel can be composed of thousands and/or millions of threads t. Type On Chip Cacheable Instructions Visibility g Latency Registers Yes No Load/Store Thread 1 cycle Shared-L1 Yes No Load/Store Block 5 cycles Constant No Yes Load Kernels 100 cycles Texture No Yes Load/Store Kernel 100 cycles Local No Yes Load/Store Thread 100 cycles Cache L2 No Yes Load/Store Kernel 250 cycles Global No Yes Load/Store Kernel 500 cycles Table: Memory types in GPUs supported by CUDA (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 6 / 32
  9. 9. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison RoadMap of architectures of GPUs NVIDIA In modern GPUs the comsumption of energy is a important restriction. Projects of GPUs are generally highly scalable. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 7 / 32
  10. 10. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison RoadMap of architectures of GPUs NVIDIA Compute Capability is a diferentiation between architectures and models of GPUs NVIDIA. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 8 / 32
  11. 11. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Compute Unified Device Architecture CUDA - Compute Unified Device Architecture CUDA is a extention of the language C, it allows to control the execution of grids in a GPU and manages its memory. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 9 / 32
  12. 12. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison GPU Programming Model A GPU Aplication is organized in grids, blocks and threads. Threads are grouped in blocks and they are grouped in a grid. Linear translation to know the Id of a thread in a grid. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 10 / 32
  13. 13. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Top 500 Supercomputers Intel Core i7 990X: 6 cores, US$ 1000 Theoretical maximum performance 0.4 TFLOP GTX680: 1500 cores and 2GB, pre¸co US$500 Theoretical maximum performance 3.0 TFLOP Accelerators and co-processors in the ranking top 500 Supercomputers more powerful of the world (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 11 / 32
  14. 14. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Top 500 Green Supercomputers $$$$$$ Ranking of the supercomputers more efficient energetically in the world. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 12 / 32
  15. 15. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison BSP-based model Vs. Machine Learning 1 Introduction and Motivation 2 Parallel Programming Models 3 Machine Learning Techniques 4 Comparison (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 12 / 32
  16. 16. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Amdahl’s law and Flynn’s Taxonomy Flynn’s Taxonomy - 1966 Single Instruction Multiple Instruction Single Data SISD - Sequential MISD Multiple Data SIMD [SIMT] - GPU MIMD - Multicore Amdahl’s law - 1967 Amdahl’s law gives the theoretical speedup of the execution of a task at fixed workload that can be expected of a system whose resources are improved. Speedup: S = Speed-up P = Number of Processors T = Time Sp = T1 Tp (1) (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 13 / 32
  17. 17. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Parallel Random Access Machine (PRAM) Figure: PRAM Model It ignores lower level architectural constraints, and details, such as memory access contention and overhead, synchronization overhead, interconnection network throughput, connectivity, speed limits and link bandwidths, etc. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 14 / 32
  18. 18. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Bulk Synchronous Parallel Model Figure: Super-step in the BSP model The cost to execute the i-th super-step is then given by: wi + ghi + L (2) The total execution time of the applica- tion is given by: T = W + gH + LS (3) (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 15 / 32
  19. 19. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Bulk Synchronous Parallel Model Bulk Synchronous Parallel (BSP), introduced by Valiant in 1990 Turing Award 2010. High Level model for parallelism Computation and communication of a Kernel function We did not include the synchronization step, nei- ther communication with host memory Optimization aspects are modeled by adjusting a single parameter λ Leslie Valiant (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 16 / 32
  20. 20. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Analytical Model Published Divergence, optimizations in the communication and differences between architecture are adjusted by one parameter, λ1 Tk = t · (Comp + CommSM + CommGM) R · P · λ (4) CommGM = (ld1 + st1 − L1 − L2) · gGM + L1 · gL1 + L2 · gL2 (5) CommSM = (ld0 + st0) · gSM (6) comp, ld0, st0, ld1 and st1 are obtained on the source code. L1 and L2 Cache hits are captured by profiling. 1 M. Amaris, D. Cordeiro, A. Goldman, and R. Y. Camargo, “A simple bsp-based model to predict execution time in gpu applications,” in 22nd Int’l Conference on HPC, December 2015 (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 17 / 32
  21. 21. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison BSP-based model Vs. Machine Learning 1 Introduction and Motivation 2 Parallel Programming Models 3 Machine Learning Techniques 4 Comparison (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 17 / 32
  22. 22. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Machine Learning Techniques The theoretical subject of “learning” is related to prediction. Supervised Learning Unsupervised Learning 3 different Machine Learning Techniques Simple Linear Regression (LR) Support Vector Machines (SVM) Random Forest (RF) In this work, we wanted to use simple models to prove that they achieve reasonable predictions. Fair comparison: (Data Input - Profile Information). (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 18 / 32
  23. 23. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Linear Regression (LR) It assumes that there is approximately a linear relationship between each Xp and Y . Mathematically, we can write the multiple linear regression model as Y ≈ β0 + β1X1 + +β2X2 + . . . + +βpXp + (7) where Xp represents the pth predictor and βp quantifies the association between that variable and the response. Figure: Example of a Linear Regression (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 19 / 32
  24. 24. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Support Vector Machines (SVM) SVM belongs to the general category of kernel methods, which are algo- rithms that depend on the data only through dot-products. The dot product can be replaced by a kernel function which computes a dot product in some possibly high dimensional feature space Z. It maps the input vector x into the feature space Z. Figure: Example of Linear and no linear kernel for SVM in classification (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 20 / 32
  25. 25. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Support Vector Machines (SVM) SVM belongs to the general category of kernel methods, which are algo- rithms that depend on the data only through dot-products. The dot product can be replaced by a kernel function which computes a dot product in some possibly high dimensional feature space Z. It maps the input vector x into the feature space Z. Figure: Example of Linear and no linear kernel for SVM in regression (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 20 / 32
  26. 26. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Random Forest (RF) Random Forests belong to decision tree methods, capable of performing both regression and classification tasks. Figure: Diagram of a tree decision (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 21 / 32
  27. 27. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison BSP-based model Vs. Machine Learning 1 Introduction and Motivation 2 Parallel Programming Models 3 Machine Learning Techniques 4 Comparison (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 21 / 32
  28. 28. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison GPUs of the Testbed Model C.C. Memory Bus Bandwidth L2 Cores/SM Clock GTX-680 3.0 2 GB 256-bit 192.2 GB/s 0.5 M 1536/8 1058 Mhz Tesla-K40 3.5 12 GB 384-bit 276.5 GB/s 1.5 MB 2880/15 745 Mhz Tesla-K20 3.5 4 GB 320-bit 200 GB/s 1 MB 2496/31 706 MHz Titan Black 3.5 6 GB 384-bit 336 GB/s 1.5 MB 2880/15 980 Mhz Titan 3.5 6 GB 384-bit 288.4 GB/s 1.5 MB 2688/14 876 Mhz Quadro K5200 3.5 8 GB 256-bit 192.2 Gb/s 1 MB 2304/12 771 Mhz Titan X 5.2 12 GB 384-bit 336.5 GB/s 3 MB 3072/24 1076 Mhz GTX-980 5.2 4 GB 256-bit 224.3 GB/s 2 MB 2048/16 1216 Mhz GTX-970 5.2 4 GB 256-bit 224.3 GB/s 1.75 MB 1664/13 1279 Mhz Table: Hardware specifications of the GPUs in the testbed (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 22 / 32
  29. 29. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Algorithm Testbed 9 different applications Matrix Multiplications in 4 different optimizations: * Global Memory - MMGU * Global Memory with coalesced accesses - MMGC * Global and Shared Memory - MMSU * Global and shared Memory with coalesced accesses - MMSC Matrix Addition in 2 different optimizations: * Global Memory - MAU * Global Memory with coalesced accesses - MAC Dot Product - dotP Vector Addition - vAdd Maximum Subarray Problem - MSA (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 23 / 32
  30. 30. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Dataset 10 Times each sample, with a confidence interval of 95%. First Scenario - Machine Learning Vs Machine Learning 1st MMSC with Block size 42, 82, 122, 162, 202, 242, 282, and 322. 256 samples per GPU. More 2000 Samples. Second Scenario - Analytical Model Vs Machine Learning Analytical Model 1D App. with input sizes from 218 until 227. 10 per GPU. 90 Samples. 2D App. with input sizes from 28 to 213. 6 per GPU. 54 Samples Machine Learning - Block size 82, 162 and 322. 1D App. with input sizes from 218 to 227. 207 per GPU. 1863 Samples. 2D App. with input sizes from 28 to 213. 96 per GPU. 864 Samples MSA Blocksize 128. 96 samples per GPU. 864 Samples. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 24 / 32
  31. 31. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Features of the Machine Learning Techniques 13 features were used to feed the Machine learning Techniques. Feature Description num of cores Number of cores per GPU max clock rate GPU Max Clock rate Bandwidth Theoretical Bandwidth Input Size Size of the problem totalLoadGM Load transaction in Global Memory totalStoreGM Store transaction in Global Memory TotalLoadSM Load transaction in Shared Memory TotalStoreSM Store transaction in Global Memory FLOPS SP Floating operation in Single Precision BlockSize Number of threads per blocks GridSize Number of blocks in the kernel No. threads Number of threads in the applications Achieved Occupancy Ratio of the average active warps per active cycle to the maximum number of warps ed on a multiprocessor. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 25 / 32
  32. 32. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Use Cases of the Analytical Model Par. Matrix Multiplication Matrix Addition vAdd dotP MSA MMGU MMGC MMSU MMSC MAU MAC comp N· FMA 1 · 24 1 · 96 (N/t) · 100 ld1 2 · N 2 2 N/t st1 1 1 1 5 ld0 0 2 · N 0 0 N/t st0 0 1 0 1 + log(t) 5 q q q q q 0 10 20 30 40 50 60 70 80 90 100 110 120 130 MMGU MMGC MMSU MMSC MAU MAC dotP vAdd MSA Applications LambdaValues Lambda Values of each one of the Applications (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 26 / 32
  33. 33. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Log transformation We first transformed the data to a log2 scale and, after performing the learning and predictions, we returned to the original scale using a 2pred transformation2, reducing the non-linearity effects. Figure: Quantile-Quantile Analysis of the generated models 2 B. J. Barnes, et al. “A regression-based approach to scalability prediction,” in Proceedings of the 22Nd Annual Int’l Conference on Supercomputing, ser. ICS ’08. New York, NY, USA. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 27 / 32
  34. 34. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Results Machine Learning - 1st Scenario Tesla K40 Tesla K20 Quadro Titan TitanBlack TitanX GTX 680 GTX 980 GTX 970 ●●● ●● ● ● ●● ● ● ● ●●●●●● ●● ●● ●●●● ● ● ●●● ● ●● 0.0 0.5 1.0 1.5 2.0 2.5 AccuracyTkTm Linear Regression of MMSC ●● ●●●●●●●●●●●●●● ●● ● ●● ●●●●●●●●●●●●●● ● ● ● ● 0.0 0.5 1.0 1.5 2.0 2.5 AccuracyTkTm Support Vector Machines of MMSC ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ●●● ● ● ●●●●● ●●●● ●●● ●●●● 0.0 0.5 1.0 1.5 2.0 2.5 AccuracyTkTm Random Forest of MMSC Figure: Accuracy of Machine Learning Algorithms of matMul-SM-Coalesced with many samples (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 28 / 32
  35. 35. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Results Machine Learning VS Analytical Model Analytical LM RF SVM 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5 MMGUMMGCMMSUMMSCMAU uracyTkTm Accuracy of the compared techniques 0.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 MAUMACdotPvAddMSA AccuracyTkTm G p u s Tesla-K40 Tesla-K20 Quadro Titan TitanBlack TitanX GTX-680 GTX-980 GTX-970 (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 29 / 32
  36. 36. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Results Machine Learning VS Analytical Model Analytical LM RF SVM 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 MMGUMMGCMMSUMMSCMAUMAC AccuracyTkTm Accuracy of the compared techniques 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 MMSUMMSCMAUMACdotPvAddMSA AccuracyTkTm G p u s Tesla-K40 Tesla-K20 Quadro Titan TitanBlack TitanX GTX-680 GTX-980 GTX-970 (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 30 / 32
  37. 37. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Conclusions Fair comparison. Analytical model requires calculations Machine learning provides more flexibility and generalization Linear Regression can do reasonable predictions But, ML requires a lot of label samples (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 31 / 32
  38. 38. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Future Works Irregular benchmarks (Rodinia, SHOC). Multiple kernels our GPUS and global synchronization One extra memory level, the CPU RAM. Feature extraction. (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 32 / 32
  39. 39. Introduction and Motivation Parallel Programming Models Machine Learning Techniques Comparison Thanks for your attention Repository of the work: https://github.com/marcosamaris/svm-gpuperf (gold, amaris)@ime.usp.br (IME - USP) BSP-based model Vs. Machine Learning December, 2016 32 / 32

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

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