| Authors |
Steinberger Markus, Kenzel Michael, Kainz Bernhard, Schmalstieg Dieter |
| Appeared in |
Proceedings of Innovative Parallel Computing (InPar'12) |
| Date |
May 2012 |
| 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. |
| Link |
PDF |
