The Embedded Genetic Allocator-a system to automatically optimize the use of memory resources in high performance, scalable computing systems

Abstract

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Describes an approach to the problem of optimizing memory resource use in high-performance scalable computing systems. The approach is automatic and applicable to a wide variety of system architectures. It consists of a hybrid genetic algorithm optimizer (the Embedded Genetic Allocator or EGA), coupled with a high-precision software performance monitoring system. EGA requires no programmer knowledge of the underlying non-uniform memory access (NUMA) architecture of the target hardware; the programmer simply specifies the data buffers to be allocated and requires that certain groups of buffers share the same performance quality. EGA minimizes the execution time of time-critical portions of the target system by allocating target program data buffers to various memory banks in the NUMA architecture. These trial allocations are loaded and evaluated directly on the target hardware. Measurements of process execution time are derived from synchronized event logging of multiple processors performed by BBN TraceMaker/sup TM/. The timing data provides the information for an optimizer cost function which the genetic algorithm uses when selecting from amongst competing allocations. Thus, EGA automates the trial-and-error method of hand optimization. We have demonstrated EGA performing memory allocation optimizations on a typical VME-based multiprocessor DSP system and completed a set of optimization experiments. The results demonstrate that EGA can be used to optimize the memory allocation on a DSP with real-world code, and that the resulting optimizations can rival those generated manually by a skilled programmer.