ScatterAlloc: Massively parallel dynamic memory allocation for the GPU
File(s)
Accepted version
Author(s)
Steinberger, M
Kenzel, M
Kainz, B
Schmalstieg, D
Type
Conference Paper
Abstract
In this paper, we analyze the special requirements of a dynamic memory allocator that is designed for massively parallel architectures such as Graphics Processing Units (GPUs). We show that traditional strategies, which work well on CPUs, are not well suited for the use on GPUs and present the thorough design of ScatterAlloc, which can efficiently deal with hundreds of requests in parallel. Our allocator greatly reduces collisions and congestion by scattering memory requests based on hashing. We analyze ScatterAlloc in terms of allocation speed, data access time and fragmentation, and compare it to current state-of-the-art allocators, including the one provided with the NVIDIA CUDA toolkit. Our results show, that ScatterAlloc clearly outperforms these other approaches, yielding speed-ups between 10 to 100.
Date Issued
2012-05-14
Date Acceptance
2012-05-13
Citation
Innovative Parallel Computing (InPar), 2012, 2012, pp.1-10
ISBN
978-1-4673-2632-2
Publisher
IEEE
Start Page
1
End Page
10
Journal / Book Title
Innovative Parallel Computing (InPar), 2012
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
Innovative Parallel Computing (InPar)
Start Date
2012-05-13
Finish Date
2012-05-14
Coverage Spatial
San Jose, CA