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Computing Platform Technology Division in CTO MediaTek, Inc.

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1 Computing Platform Technology Division in CTO MediaTek, Inc.
Heterogeneous Computing for Smart and Energy-efficient Mobile Applications Ting-Chang Huang Senior Engineer Computing Platform Technology Division in CTO MediaTek, Inc. May 4, 2015 – NTHU, HsinChu

2 Heterogeneous Computing
Symmetric Computing Asymmetric Computing Heterogeneous Computing Symmetric Computing Asymmetric Computing Heterogeneous Computing Symmetric Computing Asymmetric Computing Heterogeneous Computing Power Consumption Little Core Household Applications Light-load Tasks Big Core Serial Computing Heavy-load Tasks Little Core Household Applications Light-load Tasks GPU Intensive Computing Parallel Algorithms Big Core Serial Computing Heavy-load Tasks Little Core Household Applications Light-load Tasks Big Core GPU Change color of GPU to green Little Core Performance (MIPS)

3 Heterogeneous computing and OpenCL
OpenCL supports functional portability OpenCL doss not guarantee performance portability

4 Evolution of MediaTek CorePilot
Motivation Challenge Solution Achievements Conclusion Evolution of MediaTek CorePilot Task Scheduler Interactive Power Management Adaptive Thermal Management Presented at Linley Mobile Conf’14 2.0 Task Scheduler Interactive Power Management Adaptive Thermal Management Device Fusion enlarge the dialog boxs May

5 Benefits of GPU Computing
Motivation Challenge Solution Achievements Conclusion Benefits of GPU Computing peak power and performance of MT6795 Higher performance Lower power consumption Mobile applications: Computer vision Image processing Video acceleration Cognitive computing -50% +25% H.265 decoder acceleration stereo matching Image processing: object removal / face beautification Computer vision: pedestrian/face detection, stereo matching (build 3D model from 2D pictures) Video acceleration: H.265 decoder acceleration, Align the image border Typo correction object removal face beautification face detection pedestrian detection Roy Ju 4/22 & 23/2015

6 Computation Intensive Applications: Deep Learning
Motivation Challenge Solution Achievements Conclusion Computation Intensive Applications: Deep Learning Image Voice Data Layer 1. Pixels features Input Layer Layer 2. Edges and Simple shapes Hidden Layers Output Layer Layer 3. Complex shapes Why “Voice” as an input? What’s the difference between “Image” and “Data” Layer 4. Object Models Roy Ju 4/22 & 23/2015

7 Deep Learning in Object Recognition
Motivation Challenge Solution Achievements Conclusion Deep Learning in Object Recognition AlexNet (based on ImageNet dataset) hidden layers Recognize 1000 objects Large amount of computation overwhelms CPU. FPS Power Power efficiency Eigen CPU OpenCL CPU OpenCL GPU 1. Scales in FPS and power efficiency figures are changed as normalized value 2. Remove of ILSVRC’12 , and Add “(based on ImageNet dataset)” Normalized value Roy Ju 4/22 & 23/2015

8 Deep Learning Demo on MT6795 GPU
Motivation Challenge Solution Achievements Conclusion Deep Learning Demo on MT6795 GPU Add “on MT6795 GPU” Roy Ju 4/22 & 23/2015

9 Sticking to One isn’t Always the Best
Motivation Challenge Solution Achievements Conclusion Sticking to One isn’t Always the Best GPU > CPU GPU < CPU Add animation for CPU+GPU Description of JuliaSet “a mathematic fractal computing workload” Utilizing CPU+GPU outperforms single devices A need of portable programming APIs across devices Roy Ju 4/22 & 23/2015

10 Not all programs run well on GPU
Currently, programmers determine a program being executed at CPU or GPU However, programmers usually cannot determine the suitable device, leading to… lower performance higher energy consumption JuliaSet  suitable for GPU ViolaJones  suitable for CPU

11 WHY some programs performs slower on GPU?
Program affinity varies with the implementation Different device computing capability Factors which affect affinity: number of TLP divergence barrier memory access pattern …… find_neighbor in Super Resolution Factors which computing capability: Hardware configuration Thermal limitation Different GPU architectures ……

12 Heterogeneous Computing Product Release
Motivation Challenge Solution Achievements Conclusion Heterogeneous Computing Product Release 2014 2015 OpenCL 1.2 GPU OpenCL 1.2 CPU OpenCL 1.2 Device Fusion MT6595/IMG 6XT MT6795/IMG 6XT MT6795/CA53x8 MT6795/CA53x8, IMG 6XT (CPU+GPU) OpenCL Programs GPU CPU Device Fusion OpenCL is an industry standard programming API for heterogeneous computing. OCL 2.0 : (1) Unified share virtual memory and (2) fully coherency => high efficient data movement between CPU & GPU clusters Lowlight the description block of “OpenCL….” and “CPU and GPU …” due to Barz’s comments Efficiently execute OpenCL programs by fusing GPU and CPU computing capability. Key addition to CorePilot 2.0 CPU and GPU devices are the pillars and also available standalone. Roy Ju 4/22 & 23/2015

13 Device Fusion Efficiently execute OpenCL programs by fusing GPU and CPU computing capability Throughput-oriented OpenCL Program GPU CPU Device Fusion GPU-preferred OpenCL Program GPU CPU Device Fusion CPU-preferred OpenCL Program GPU CPU Device Fusion single-device execution parallel execution

14 Architecture Overview of Device Fusion
Motivation Challenge Solution Achievements Conclusion Architecture Overview of Device Fusion OpenCL API Wrapper & Dispatcher Provide user-friendly transparency to hide implementation detail OpenCL Programs Device Fusion OpenCL API Wrapper Profiler Collect app dynamic behaviors and underlying system and device info Profiler Parallel Infrastructure Dispatch Policy Maker Dispatch Policy Maker Make intelligent decisions to dispatch based on profiling information Dispatcher Change the color of description blocks according to Barz’s comments CPU GPU Parallel Infrastructure Automate data partitioning and synchronizations for parallel execution

15 Case Study: Face Detection
Motivation Challenge Solution Achievements Conclusion Case Study: Face Detection CreateIntensity IntegralStep1 Transpose IntegralStep2 Execution Flow CPU GPU ViolaJones CPU + GPU (parallel) +186% to GPU +42% to CPU +146% to GPU +22% to CPU -17% to GPU -18% to CPU Change the description “CPU+GPU”  “CPU+GPU (parallel)” Change the color of block “CPU+GPU (parallel)” Correct the type “normalized” -45% to GPU -46% to CPU Roy Ju 4/22 & 23/2015

16 Usage scenarios and case studies – parallel execution
For a kernel, enable parallel execution of both CPU and GPU Scenario: throughput-oriented kernel 64% jobs at GPU 36% jobs at CPU 40% jobs at GPU 60% jobs at CPU

17 Performance of Device Fusion
Motivation Challenge Solution Achievements Conclusion Performance of Device Fusion Measured on MT6795 Compubench: Professional OpenCL benchmark Matrix_Mul: GPU-favored workload DCT: CPU-favored workload Boxfilter: unable to be parallel executed, choose the device with better performance (i.e. GPU) Biliteral NR: good case for parallel execution since similar CPU/GPU capability Roy Ju 4/22 & 23/2015

18 Thermal-aware Scheduler
Motivation Challenge Solution Achievements Conclusion Thermal-aware Scheduler Overheating slows down the SoC speed, leading to dramatic and un-predictable performance drops Thermal-aware scheduler reduces performance drops Re-dispatching jobs to a device with better power-efficiency Lowering DVFS overheating Roy Ju 4/22 & 23/2015

19 Thermal-aware scheduler
Reduce the parallelism when temperature is high When thermal condition deteriorates, reduce the parallelism step-by-step to mitigate the hotspot Temp. threshold 2 Temp. threshold 1 Temp. threshold 0 hot cool Use learned ratio ratio is automatically changed due to CPU throttling Force ratio as 1.0 (all GPU) or 0.0 (all CPU) Force ratio as 1.0 (all GPU) Try to mitigate the hotspot by parallelism reduction Reducing CPU load to relax the thermal condition

20 Performance is only topic in heterogeneous computing?
Roy Ju 4/22 & 23/2015

21 Performance of Device Fusion (performance mode)
Pow+ 283% Perf + 40% Pow+ 34% 1.5W 5.5W 4.1W 3.4W 1.2W 4.6W Copyright © MediaTek Inc. All rights reserved.

22 Power-aware scheduling in Device Fusion
GPU power constraint best performance lowest power Performance requirement

23 Face detection: peak power and performance under different CPU/GPU configurations
power budget = 3W Use power budget = 1.5W Use

24 Face detection: peak power and performance under different CPU/GPU configurations
use this configuration performance constraint = 1.2FPS

25 Using power-aware scheduling under Face detection

26 Using power-aware scheduling under Face detection

27 MediaTek CorePilot 2.0 1st Mobile OpenCL Device Fusion
Motivation Challenge Solution Achievements Conclusion MediaTek CorePilot 2.0 1st Mobile OpenCL Device Fusion Introduced the first mobile OpenCL Device Fusion into production in January, 2015 Extending Technology Leadership Advance the performance, power, and thermal management techniques in CorePilot into the extended framework in CorePilot 2.0. Outstanding Performance 20% performance uplift in the CompuBenchCL benchmark by using Device Fusion Enhancing User Experiences Complete OpenCL solutions on CPU and GPU to enable smart and energy-efficient mobile applications Roy Ju 4/22 & 23/2015

28 Summer Intern/part-time employee/Permanent employee
Roy Ju 4/22 & 23/2015

29

30 Dual-channel LPDDR3 933MHz
MT6795 MT6795 Schedule MP 2015/1 Android OS Android L 5.0 CPU CA53 Octa-Core up to 2.2 GHz Graphic IMG G MHz Memory Dual-channel LPDDR3 933MHz

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