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Sun 23 Jun 2019 10:10 - 10:35 at 106A - Scaling Up

Memory fragmentation is a widely studied problem of dynamic memory allocators. It is well known that fragmentation can lead to premature out-of-memory errors and poor cache performance.

With the recent emergence of dynamic memory allocators for SIMD accelerators, memory fragmentation is becoming an increasingly important problem on such architectures. Nevertheless, it has received little attention so far. Memory-bound applications on SIMD architectures such as GPUs can experience an additional slowdown due to less efficient vector load/store instructions.

We propose CompactGpu, an incremental, fully-parallel, in-place memory defragmentation system for GPUs. CompactGpu is an extension to the DynaSOAr dynamic memory allocator and defragments the heap in a fully parallel fashion by merging partly occupied memory blocks. We developed several implementation techniques for memory defragmentation that are efficient on SIMD/GPU architectures, such as finding defragmentation block candidates and fast pointer rewriting based on bitmaps.

Benchmarks indicate that our implementation is very fast with typically higher performance gains than compaction overheads. It can also decrease the overall memory usage.

Sun 23 Jun

Displayed time zone: Tijuana, Baja California change

09:00 - 11:00
Scaling UpISMM 2019 at 106A
09:00
5m
Day opening
Welcome from the chairs
ISMM 2019
Harry Xu University of California, Los Angeles (UCLA), Jeremy Singer University of Glasgow
09:05
40m
Talk
Keynote 1: Relaxed memory ordering needs a better specification
ISMM 2019
09:45
25m
Talk
Automatic GPU Memory Management for Large Neural Models in TensorFlow
ISMM 2019
Tung D. Le IBM Research - Tokyo, Haruki Imai IBM Research - Tokyo, Yasushi Negishi IBM Research - Tokyo, Kiyokuni Kawachiya IBM Research - Tokyo
10:10
25m
Talk
Massively Parallel GPU Memory Compaction
ISMM 2019
Matthias Springer Tokyo Institute of Technology, Hidehiko Masuhara Tokyo Institute of Technology
10:35
25m
Talk
Scaling Up Parallel GC Work-Stealing in Many-Core Environments
ISMM 2019
Michihiro Horie IBM Research - Tokyo, Kazunori Ogata IBM Research, Japan, Mikio Takeuchi IBM Research - Tokyo, Hiroshi Horii IBM Research, Japan