MAPS

Autor:	Eri Rubin, Ely Levy, Tal Ben-Nun, Amnon Barak
Rok vydání:	2014
Předmět:	Distributed shared memory Computer science Uniform memory access Parallel computing Memory map Non-uniform memory access Physical address Shared memory Computer architecture Hardware and Architecture CUDA Pinned memory Massively parallel Software Information Systems
Zdroj:	ACM Transactions on Architecture and Code Optimization. 11:1-22
ISSN:	1544-3973 1544-3566
DOI:	10.1145/2680544
Popis:	GPUs play an increasingly important role in high-performance computing. While developing naive code is straightforward, optimizing massively parallel applications requires deep understanding of the underlying architecture. The developer must struggle with complex index calculations and manual memory transfers. This article classifies memory access patterns used in most parallel algorithms, based on Berkeley’s Parallel “Dwarfs.” It then proposes the MAPS framework, a device-level memory abstraction that facilitates memory access on GPUs, alleviating complex indexing using on-device containers and iterators. This article presents an implementation of MAPS and shows that its performance is comparable to carefully optimized implementations of real-world applications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0d77908fb58b469ecd85e0d333f71ec7 https://doi.org/10.1145/2680544 Zobrazit plný text záznamu