Exploration of OpenCL Heterogeneous Programming for Porting Solidification Modeling to CPU‐GPU Platforms

Self Cite

Summary This work is a part of the global tendency to use modern computing systems for modeling the phase‐field phenomena. The main goal of this article is to improve the performance of a parallel application for the solidification modeling, assuming the dynamic intensity of computations in successive time steps when calculations are performed using a carefully selected group of nodes in the grid. A two‐step method is proposed to optimize the application for multi‐/manycore architectures. In the first step, the loop fusion is used to execute all kernels in a single nested loop and reduce the number of conditional operators. These modifications are vital to implementing the second step, which includes an algorithm for the dynamic workload prediction and load balancing across cores of a computing platform. Two versions of the algorithm are proposed—with the 1D and 2D maps used for predicting the computational domain within the grid. The proposed optimizations allow increasing the application performance significantly for all tested configurations of computing resources. The highest performance gain is achieved for two Intel Xeon Platinum 8180 CPUs, where the new code based on the 2D map yields the speedup of up to 2.74 times, while the usage of the proposed method with the 2D map for a single KNL accelerator permits reducing the execution time up to 1.91 times.

Section: Discussionmentioning

confidence: 99%

Dynamic workload prediction and distribution in numerical modeling of solidification on multi‐/manycore architectures

Halbiniak

Olas

Szustak

et al. 2020

Self Cite

“…In both works, only NVIDIA GPUs are used-either with Fermi architecture 28 or Kepler and Volta architectures. 24 Moreover, the studied platforms employ no more than two accelerators per node. For parallelization of the phase-field method across multiple nodes, each containing only a single GPU, Tennyson et al 27 used the fusion of OpenCL and MPI programming interfaces.…”

Section: Related Workmentioning

confidence: 99%

“…The ultimate goal 39 is taking advantage of these powerful platforms without having to learn the hardware details or significantly change the application codes. Numerous programming models and environments have been developed, including CUDA, 11 HIP, 12 OpenCL, 24 and Kokkos. 40 An alternative option is directive-based models such as OpenACC 41 and OpenMP.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Single‐ and multi‐GPU computing on NVIDIA‐ and AMD‐based server platforms for solidification modeling application

Halbiniak,

Meyer,

Rojek

2023

Self Cite

SummaryThis work explores the performance of single‐ and multi‐GPU computing on state‐of‐the‐art NVIDIA‐ and AMD‐based server‐class hardware using various programming interfaces to accelerate a real‐world scientific application for solidification modeling based on the phase‐field method. The main computations of this memory‐bound application correspond to 20 stencils computed across grid nodes. We investigate the application's scalability for two basic schemes of organizing computation: without and with hiding data transfers behind computation, combined with using either peer‐to‐peer inter‐GPU data transfers through NVIDIA NVLink and AMD Infinity interconnects or communication over the PCIe and main memory. Among the studied programming interfaces is CUDA, HIP, and OpenMP Accelerator Model. While the first two are designed to write the codes for a specific hardware platform, OpenMP enables code portability between NVIDIA and AMD GPUs. The resulting performance is experimentally assessed on computing platforms containing NVIDIA V100 (up to 8 GPUs) and A100 (one GPU), as well as AMD MI210 (one device) and MI250 (up to 8 logical GPUs).

“…Finally, they discover that utilizing typical memory allocation methods on SoCs double the required memory because of unnecessary device memory copies, despite being physically shared with host memory. The tests show that GPU application memory usage can be reduced up to 50% and that even performance improvements can occur just by replacing standard memory allocation and memory copy methods with managed unified memory, or pinned memory allocation. In Reference 9, the authors provide a comprehensive study of OpenCL heterogeneous programming for porting applications to CPU‐GPU computing platforms, with a real‐life application for the solidification modeling . The aim is to achieve a flexible workload distribution between available CPU‐GPU resources and optimize application performance.…”

Section: Contents Of the Special Issuementioning

confidence: 99%

Special Issue on High‐end Heterogeneous Architectures, Methodologies, and Algorithms (HHAMA20)

Kosta

Laccetti

Lapegna

et al. 2020

We are experiencing painful months, and as we write this editorial, the COVID-19 pandemic is still ongoing, severely affecting the peoples of many researchers who participated in this special issue. Our first thought is for the victims of this tragedy. Despite the difficulties, we have worked hard to keep in touch with the authors of the manuscripts, the anonymous referees, and the journal staff, so now we are especially delighted to present this special issue High-end Heterogeneous Architectures, Methodologies, and Algorithms (HHAMA-20) hosted on the Concurrency and Computation: Practice and Experience journal. The special issue collects an extension of the most valuable works presented at the Fifth Workshop on Models, Algorithms and Methodologies for Hybrid Parallelism in New HPC Systems (MAMHYP-19), held in Bialystok (Poland) in September 2019, jointly with the 13th Conference on Parallel Processing and Applied Mathematics (PPAM-19). New original papers related to the workshop themes are also included. The final aim is to provide a glimpse of the current state of knowledge related to the development of efficient methodologies and algorithms for HPC systems with multiple forms of parallelism. 1 CONTENTS OF THE SPECIAL ISSUE In this special issue, we collected nine papers, selected from 13 works submitted from eight different countries, with an acceptance rate of about 70%. Each manuscript has been accepted after two or three review rounds. In the first round, each paper has been assigned to two external anonymous reviewers. To the doubtful cases (i.e., borderline papers or with diverging recommendations), the guest editors of the special issue have been given additional individual attention. Authors of papers selected in the first round have been asked to revise their work according to the reviewers' feedback and submit a revised manuscript for a second or third review. The papers cover a broad spectrum of topics, and they could be classified into two large categories: