Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
hiCUDA: a high-level directive-based language for GPU programming
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
A cross-input adaptive framework for GPU program optimizations
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Model-driven autotuning of sparse matrix-vector multiply on GPUs
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Implementing the PGI Accelerator model
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
MapCG: writing parallel program portable between CPU and GPU
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
OpenMPC: Extended OpenMP Programming and Tuning for GPUs
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Sponge: portable stream programming on graphics engines
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units
Automatic C-to-CUDA code generation for affine programs
CC'10/ETAPS'10 Proceedings of the 19th joint European conference on Theory and Practice of Software, international conference on Compiler Construction
Compiling a high-level language for GPUs: (via language support for architectures and compilers)
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Adaptive input-aware compilation for graphics engines
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
OpenACC: first experiences with real-world applications
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
accULL: an OpenACC implementation with CUDA and OpenCL support
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
On Expressing Strategies for Directive-Driven Multicore Programing Models
Proceedings of Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms
Exploiting GPU Hardware Saturation for Fast Compiler Optimization
Proceedings of Workshop on General Purpose Processing Using GPUs
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The difficulties posed by GPGPU programming and the need to increase productivity have guided research towards directive-based high-level programs for accelerators. This effort has led to the definition of the OpenACC industry standard. It significantly simplifies writing code for graphics engines leaving the programmer the opportunity to tune the application for the target hardware and input. In this paper we address the problem of choosing the best mapping of sequential OpenACC loops to the parallel thread-space for a given program and input size. We show that auto-tuning on mapping parameters can improve performance by up to 4.8x over the default chosen by a state-of-the-art compiler. To reduce the overhead of auto-tuning we introduce a search technique that exploits similarities in behaviour across inputs using a nearest neighbour approach. This dramatically reduces the search for a good mapping (by 97% compared to random search). Finally we propose a heuristic for stopping the focused search which, averaged across 12 benchmarks and 30 input sizes each, achieves a speedup over the default of 1.26x with only 8 sampling runs.