Automatic Profiling and Optimizations of Applications

Improving Data Layout at Memory Allocation (Runtime)

Phenomenon Research

• reduce-false-sharing: variation of cache miss rate w.r.t. cache block size and kind of variables

Better Allocators

• hoard: Hoard allocator, using shared heap + per-process heaps

• reduce-false-sharing: several optimizations of data layout.

  • SplitScalar: Place scalar variables into different blocks (their ideal architecture has control over cache blocks)

  • HeapAllocate: Allocate from different heap regions for different processors

  • Expand Record: Pad between fields of a record (struct).

  • Align Record: Choose a layout for arrays of records that minimizes the number of blocks the average record spans.

  • LockScalar: Place active scalars with a lock in the same block as the lock variable.

Hardware Acceleration

• mallacc: dedicated hardware for allocator operations

Allocator Evaluations

• alloc-experiment: compared 7 allocators comprehensively and statistically significantly.

• cache-locality: early paper, studied some poorly-designed allocator and showed allocators may impact perfomance by increasing paging and cache miss rates.

Ignored Keywords

Algorithm for parallel memory allocation

• Some ancient papers focus on this. We should already have this in modern allocators.

Buddy system/algorithm

• This minimizes little bits of allocator memory overhead, which is of more interest when memory was tight.

Improving Data Layout at Compile Time

Improving Text Layout