- author = "Usman Dastgeer and Johan Enmyren and Christoph W. Kessler",
- title = "Auto-tuning {SkePU}: A Multi-backend Skeleton Programming Framework for {multi-GPU} Systems",
- booktitle = "Proceedings of the 4th International Workshop on Multicore Software Engineering, IWMSE-2011",
- year = "2011",
- editor = "Harald Gall and Nenad Medvidovic",
- pages = "25--32",
- address = "Waikiki, Honolulu, HI, USA",
- publisher_address = "New York, NY, USA",
- month = "21-28 " # may,
- acmid = "1984697",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, genetic improvement, auto-tuning, CUDA, data parallelism, GPU, openCL, skeleton programming",
- isbn13 = "978-1-4503-0577-8",
-
URL = "
http://doi.acm.org/10.1145/1984693.1984697",
-
DOI = "
doi:10.1145/1984693.1984697",
- size = "8 pages",
-
abstract = "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).",
-
notes = "Although it claims to use GP it seems to be adapting
parameters rather than code.
also known as \cite{Dastgeer:2011:ASM:1984693.1984697} http://2011.icse-conferences.org/",
