Algorithmic skeletons: structured management of parallel computation
Algorithmic skeletons: structured management of parallel computation
Patterns and skeletons for parallel and distributed computing
Patterns and skeletons for parallel and distributed computing
An Adaptive Algorithm Selection Framework for Reduction Parallelization
IEEE Transactions on Parallel and Distributed Systems
CuPP - A framework for easy CUDA integration
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Multi-target C++ implementation of parallel skeletons
Proceedings of the 8th workshop on Parallel/High-Performance Object-Oriented Scientific Computing
An adaptive performance modeling tool for GPU architectures
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
A Skeletal Parallel Framework with Fusion Optimizer for GPGPU Programming
APLAS '09 Proceedings of the 7th Asian Symposium on Programming Languages and Systems
SkePU: a multi-backend skeleton programming library for multi-GPU systems
Proceedings of the fourth international workshop on High-level parallel programming and applications
Fourth international workshop on multicore software engineering (IWMSE 2011)
Proceedings of the 33rd International Conference on Software Engineering
Optimized composition of performance-aware parallel components
Concurrency and Computation: Practice & Experience
PARTANS: An autotuning framework for stencil computation on multi-GPU systems
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
KFusion: optimizing data flow without compromising modularity
Proceedings of the 12th annual international conference on Aspect-oriented software development
Self-Configuration and Self-Optimization Autonomic Skeletons using Events
Proceedings of Programming Models and Applications on Multicores and Manycores
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SkePU is a C++ template library that provides a simple and unified interface for specifying data-parallel computations with the help of skeletons on GPUs using CUDA and OpenCL. The interface is also general enough to support other architectures, and SkePU implements both a sequential CPU and a parallel OpenMP backend. It also supports multi-GPU systems. Currently available skeletons in SkePU include map, reduce, mapreduce, map-with-overlap, maparray, and scan. The performance of SkePU generated code is comparable to that of hand-written code, even for more complex applications such as ODE solving. In this paper, we discuss initial results from auto-tuning SkePU using an off-line, machine learning approach where we adapt skeletons to a given platform using training data. The prediction mechanism at execution time uses off-line pre-calculated estimates to construct an execution plan for any desired configuration with minimal overhead. The prediction mechanism accurately predicts execution time for repetitive executions and includes a mechanism to predict execution time for user functions of different complexity. The tuning framework covers selection between different backends as well as choosing optimal parameter values for the selected backend. We will discuss our approach and initial results obtained for different skeletons (map, mapreduce, reduce).