On GPU’s viability as a middleware accelerator

Autor:	Matei Ripeanu, Elizeu Santos-Neto, Samer Al-Kiswany, Abdullah Gharaibeh
Rok vydání:	2009
Předmět:	Computer Networks and Communications Computer science Hash function Graphics processing unit computer.software_genre CUDA Computer architecture Middleware Middleware (distributed applications) Distributed data store Operating system Graphics computer Software
Zdroj:	Cluster Computing. 12:123-140
ISSN:	1573-7543 1386-7857
DOI:	10.1007/s10586-009-0076-0
Popis:	Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible cost-effective enhancement to high-performance systems. To date, most applications that exploit GPUs are specialized scientific applications. Little attention has been paid to harnessing these highly-parallel devices to support more generic functionality at the operating system or middleware level. This study starts from the hypothesis that generic middleware-level techniques that improve distributed system reliability or performance (such as content addressing, erasure coding, or data similarity detection) can be significantly accelerated using GPU support. We take a first step towards validating this hypothesis and we design StoreGPU, a library that accelerates a number of hashing-based middleware primitives popular in distributed storage system implementations. Our evaluation shows that StoreGPU enables up twenty five fold performance gains on synthetic benchmarks as well as on a high-level application: the online similarity detection between large data files.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::29f50e50233a443b5fc95e014291625b https://doi.org/10.1007/s10586-009-0076-0 Zobrazit plný text záznamu Full text from SpringerLink