Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Automated GPU support in LuNA fragmented programming system. / Nikolay, Belyaev; Perepelkin, Vladislav.
Parallel Computing Technologies - 14th International Conference, PaCT 2017, Proceedings. ed. / Malyshkin. Vol. 10421 LNCS Springer-Verlag GmbH and Co. KG, 2017. p. 272-277 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10421 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
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 -
ID: 10068664