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  1. Proceedings of the 2012 ACM SIGPLAN Workshop on Memory Systems Performance and Correctness (MSPC '12)
  2. Parallel memory defragmentation on a GPU
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Identifying optimal multicore cache hierarchies for loop-based parallel programs via reuse distance analysis
Rank idle time prediction driven last-level cache writeback
Parallel memory defragmentation on a GPU
A higher order theory of locality
Can parallel data structures rely on automatic memory managers?
Can seqlocks get along with programming language memory models?
Trace-driven simulation of memory system scheduling in multithread application
Analysis of pure methods using garbage collection
Supporting virtual memory in GPGPU without supporting precise exceptions
Towards region-based memory management for Go
A study towards optimal data layout for GPU computing
Design space exploration of memory model for heterogeneous computing
Defensive loop tiling for multi-core processor

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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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