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Memory Management for Self-Adjusting Computation Matthew A. Hammer Umut A. Acar
 

Summary: Memory Management for Self-Adjusting Computation
Matthew A. Hammer Umut A. Acar
Toyota Technological Institute at Chicago
{hammer,umut}@tti-c.org
Abstract
The cost of reclaiming space with traversal-based garbage collec-
tion is inversely proportional to the amount of free memory, i.e.,
O(1/(1 - f)), where f is the fraction of memory that is live. Con-
sequently, the cost of garbage collection can be very high when
the size of the live data remains large relative to the available
free space. Intuitively, this is because allocating a small amount
of memory space will require the garbage collector to traverse a
significant fraction of the memory only to discover little garbage.
This is unfortunate because in some application domains the size
of the memory-resident data can be generally high. This can cause
high GC overheads, especially when generational assumptions do
not hold. One such application domain is self-adjusting computa-
tion, where computations use memory-resident execution traces in
order to respond to changes to their state (e.g., inputs) efficiently.
This paper proposes memory-management techniques for self-

  

Source: Acar, Umut - Programming Languages and Systems Group, Max-Planck Institute for Software Systems

 

Collections: Computer Technologies and Information Sciences