Optimizing Memory Accesses For Spatial Computation

In this paper we present the internal representation and optimizations used by the CASH compiler for improving the memory parallelism of pointer-based programs. CASH uses an SSA-based representation for memory, which compactly summarizes both control-flow- and dependence information. In CASH, memory optimization is a four-step process: (1) first an initial, relatively coarse, representation of memory dependences is built; (2) next, unnecessary memory dependences are removed using dependence tests; (3) third, redundant memory operations are removed (4) finally, parallelism is increased by pipelining memory accesses in loops. While the first three steps above are very general, the loop pipelining transformations are particularly applicable for spatial computation, which is the primary target of CASH. The redundant memory removal optimizations presented are: load/store hoisting (subsuming partial redundancy elimination and common-subexpression elimination), load-after-store removal, ...

• Type: Conference
• Year: 2003
• Where: CGO
• Authors: Mihai Budiu, Seth Copen Goldstein
• Keywords: CGO 2003, Loop Pipelining Transformations, Memory Dependences, Redundant Memory, Software Engineering