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LCTES 2017
Wed 21 - Thu 22 June 2017 Barcelona, Spain
co-located with PLDI 2017

Heterogeneous multi-core architectures consisting of CPUs and GPUs are commonplace in today’s embedded systems. These architectures offer potential for energy efficient computing if the application task is mapped to the right core. Realizing such potential is challenging due to the complex and evolving natural of hardware and applications. This paper presents an automatic approach to map OpenCL kernels onto heterogeneous multi-cores for a given optimization criterion – whether it is faster runtime, lower energy consumption or a trade-off between them. This is achieved by developing a machine learning based approach to predict which processor to use to run the OpenCL kernel and the host program, and at what frequency the processor should operate. Instead of hand-tuning a model for each optimization metric, we use machine learning to develop a unified framework that first automatically learns the optimization heuristic for each metric off-line, and then uses the learned knowledge to schedule OpenCL kernels at runtime based on code and runtime information of the program. We apply our approach to a set of representative \OpenCL benchmarks and evaluate it on an ARM big.LITTLE mobile platform. Our approach achieves over 93% of the performance delivered by an oracle predictor. We obtain, on average, 1.2x, 1.6x, and 1.8x improvement respectively for runtime, energy consumption and the energy delay product, when compared to a state-of-the-art heterogeneous-aware OpenCL task mapping scheme.

Conference Day
Wed 21 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:50 - 12:30
Session 1: Compiler Optimization for Embedded SystemsLCTES 2017 at Vertex WS208
Chair(s): Yi WangShenzhen University
10:50
25m
Talk
AOT Vs. JIT: Impact of Profile Data on Code Quality
LCTES 2017
April W. WadeUniversity of Kansas, Prasad KulkarniUniversity of Kansas, Michael JantzUniversity of Tennessee
11:15
25m
Talk
Adaptive Optimization for OpenCL Programs on Embedded Heterogeneous Systems
LCTES 2017
Ben TaylorLancaster University, UK, Vicent Sanz MarcoLancaster University, Zheng WangLancaster University
11:40
25m
Talk
Auto-Vectorization for Image Processing DSLs
LCTES 2017
Oliver ReicheFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Christof Kobylko, Frank HannigFriedrich-Alexander University Erlangen-Nürnberg (FAU), Jürgen Teich
12:05
25m
Talk
Dynamic Translation of Structured Loads/Stores and Register Mapping for Architectures with SIMD Extensions
LCTES 2017
Sheng-Yu Fu, Ding-Yong HongInstitute of Information Science, Academia Sinica, Ping YuDepartment of Computer Science and Information Engineering, National Taiwan University, Jan-Jan WuInstitute of Information Science, Academia Sinica, Wei-Chung HsuDept. Computer Science & Information Engineering, National Taiwan University