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Title: HANDS ON WITH OPENMP4.5 and UNIFIED MEMORY: DEVELOPING APPLICATIONS FOR IBM'S HYBRID CPU+GPU SYSTEMS (PART II)

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1395517
Report Number(s):
LLNL-CONF-730616
Journal ID: ISSN 0302-9743
DOE Contract Number:
AC52-07NA27344
Resource Type:
Conference
Resource Relation:
Journal Volume: 10468; Conference: Presented at: The International Workshop on OpenMP, New York, NY, United States, Sep 21 - Sep 22, 2017
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE

Citation Formats

Grinberg, L, Bertolli, C, and Haque, R. HANDS ON WITH OPENMP4.5 and UNIFIED MEMORY: DEVELOPING APPLICATIONS FOR IBM'S HYBRID CPU+GPU SYSTEMS (PART II). United States: N. p., 2017. Web. doi:10.1007/978-3-319-65578-9_2.
Grinberg, L, Bertolli, C, & Haque, R. HANDS ON WITH OPENMP4.5 and UNIFIED MEMORY: DEVELOPING APPLICATIONS FOR IBM'S HYBRID CPU+GPU SYSTEMS (PART II). United States. doi:10.1007/978-3-319-65578-9_2.
Grinberg, L, Bertolli, C, and Haque, R. Mon . "HANDS ON WITH OPENMP4.5 and UNIFIED MEMORY: DEVELOPING APPLICATIONS FOR IBM'S HYBRID CPU+GPU SYSTEMS (PART II)". United States. doi:10.1007/978-3-319-65578-9_2. https://www.osti.gov/servlets/purl/1395517.
@article{osti_1395517,
title = {HANDS ON WITH OPENMP4.5 and UNIFIED MEMORY: DEVELOPING APPLICATIONS FOR IBM'S HYBRID CPU+GPU SYSTEMS (PART II)},
author = {Grinberg, L and Bertolli, C and Haque, R},
abstractNote = {},
doi = {10.1007/978-3-319-65578-9_2},
journal = {},
number = ,
volume = 10468,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}

Conference:
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  • Abstract not provided.
  • Aiming to fully exploit the computing power of all CPUs and all graphics processing units (GPUs) on hybrid CPU-GPU systems to solve dense linear algebra problems, in this paper we design a class of heterogeneous tile algorithms to maximize the degree of parallelism, to minimize the communication volume, and to accommodate the heterogeneity between CPUs and GPUs. The new heterogeneous tile algorithms are executed upon our decentralized dynamic scheduling runtime system, which schedules a task graph dynamically and transfers data between compute nodes automatically. The runtime system uses a new distributed task assignment protocol to solve data dependencies between tasksmore » without any coordination between processing units. By overlapping computation and communication through dynamic scheduling, we are able to attain scalable performance for the double-precision Cholesky factorization and QR factorization. Finally, our approach demonstrates a performance comparable to Intel MKL on shared-memory multicore systems and better performance than both vendor (e.g., Intel MKL) and open source libraries (e.g., StarPU) in the following three environments: heterogeneous clusters with GPUs, conventional clusters without GPUs, and shared-memory systems with multiple GPUs.« less
  • At a high level, my research interests center around designing, programming, and evaluating computer systems that use new approaches to solve interesting problems. The rapid change of technology allows a variety of different architectural approaches to computationally difficult problems, and a constantly shifting set of constraints and trends makes the solutions to these problems both challenging and interesting. One of the most important recent trends in computing has been a move to commodity parallel architectures. This sea change is motivated by the industry’s inability to continue to profitably increase performance on a single processor and instead to move to multiplemore » parallel processors. In the period of review, my most significant work has been leading a research group looking at the use of the graphics processing unit (GPU) as a general-purpose processor. GPUs can potentially deliver superior performance on a broad range of problems than their CPU counterparts, but effectively mapping complex applications to a parallel programming model with an emerging programming environment is a significant and important research problem.« less