NDLI: Parallel memory defragmentation on a GPU

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Parallel memory defragmentation on a GPU

Content Provider	ACM Digital Library
Author	Veldema, Ronald Philippsen, Michael
Abstract	High-throughput memory management techniques such as malloc/free or mark-and-sweep collectors often exhibit memory fragmentation leaving allocated objects interspersed with free memory holes. Memory defragmentation removes such holes by moving objects around in memory so that they become adjacent (compaction) and holes can be merged (coalesced) to form larger holes. However, known defragmentation techniques are slow. This paper presents a parallel solution to best-effort partial defragmentation that makes use of all available cores. The solution not only speeds up defragmentation times significantly, but it also scales for many simple cores. It can therefore even be implemented on a GPU. One problem with compaction is that it requires all references to moved objects to be retargeted to point to their new locations. This paper further improves existing work by a better identification of the parts of the heap that contain references to objects moved by the compactor and only processes these parts to find the references that are then retargeted in parallel. To demonstrate the performance of the new memory defragmentation algorithm on many-core processors, we show its performance on a modern GPU. Parallelization speeds up compaction 40 times and coalescing up to 32 times. After compaction, our algorithm only needs to process 2%--4% of the total heap to retarget references.
Starting Page	38
Ending Page	47
Page Count	10
File Format	PDF
ISBN	9781450312196
DOI	10.1145/2247684.2247693
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-06-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Garbage collection Mark and sweep Parallel Gpu
Content Type	Text
Resource Type	Article

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