TY - GEN

T1 - Automated GPU support in LuNA fragmented programming system

AU - Nikolay, Belyaev

AU - Perepelkin, Vladislav

PY - 2017/1/1

Y1 - 2017/1/1

N2 - The paper is devoted to the problem of reduction of complexity of development of numerical parallel programs for distributed memory computers with hybrid (CPU+GPU) computing nodes. The basic idea is to employ a high-level representation of an application algorithm to allow its automated execution on multicomputers with hybrid nodes without a programmer having to do low-level programming. LuNA is a programming system for numerical algorithms, which implements the idea, but only for CPU. In the paper we propose a LuNA language extension, as well as necessary run-time algorithms to support GPU utilization. For that a user only has to provide a limited number of computational GPU procedures using CUDA, while the system will take care of such associated low-level problems, as jobs scheduling, CPU-GPU data transfer, network communications and others. The algorithms developed and implemented take advantage of concerning informational dependencies of an application and support automated tuning to available hardware configuration and application input data.

AB - The paper is devoted to the problem of reduction of complexity of development of numerical parallel programs for distributed memory computers with hybrid (CPU+GPU) computing nodes. The basic idea is to employ a high-level representation of an application algorithm to allow its automated execution on multicomputers with hybrid nodes without a programmer having to do low-level programming. LuNA is a programming system for numerical algorithms, which implements the idea, but only for CPU. In the paper we propose a LuNA language extension, as well as necessary run-time algorithms to support GPU utilization. For that a user only has to provide a limited number of computational GPU procedures using CUDA, while the system will take care of such associated low-level problems, as jobs scheduling, CPU-GPU data transfer, network communications and others. The algorithms developed and implemented take advantage of concerning informational dependencies of an application and support automated tuning to available hardware configuration and application input data.

KW - Fragmented programming

KW - GPGPU

KW - Hybrid multicomputers

KW - LuNA system

KW - Parallel programming automation

UR - http://www.scopus.com/inward/record.url?scp=85028710288&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-62932-2_26

DO - 10.1007/978-3-319-62932-2_26

M3 - Conference contribution

AN - SCOPUS:85028710288

SN - 9783319629315

VL - 10421 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 272

EP - 277

BT - Parallel Computing Technologies - 14th International Conference, PaCT 2017, Proceedings

A2 - Malyshkin, null

PB - Springer-Verlag GmbH and Co. KG

T2 - 14th International Conference on Parallel Computing Technologies, PaCT 2017

Y2 - 4 September 2017 through 8 September 2017

ER -