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Abstract or Description
For a large class of scientific computing applications, the continuing growth in physical memory capacity cannot be expected to eliminate the need to perform I/O throughout their executions. For these out-of-core applications, the large and widening gap between processor performance and disk latency is a major concern. Current operating systems deliver poor performance when an application's working set does not fit in main memory. As a result, programmers who wish to solve these out-of-core problems efficiently are typically faced with the onerous task of rewriting their application to use explicit I/O operations (e.g., read/write). In many cases, the end result is that the size of physical memory determines the size of problem that can be solved.
In this dissertation, we propose and evaluate a fully-automatic technique which liberates the programmer from this task, provides high performance, and requires only minimal changes to current operating systems. In our scheme, the compiler provides the crucial information on future access patterns without burdening the programmer, the operating system supports non-binding prefetch and release hints for managing I/O in a virtual memory system, and the operating system cooperates with a run-time layer to accelerate performance by adapting to dynamic behavior and minimizing prefetch overhead. This approach maintains the abstraction of unlimited virtual memory for the programmer, gives the compiler the flexibility to aggressively insert prefetches ahead of references, and gives the operating system the flexibility to arbitrate between the competing resource demands of multiple applications.
We implemented our compiler analysis within the SUIF compiler, and used it to target implementations of our run-time and operating system support on both research and commercial systems (HURRICANE and IRIX 6.5, respectively). Our experimental results show large performance gains for out-of-core scientific applications on both systems: more than 50% of the I/O stall time has been eliminated in most cases, thus translating into overall speedups of roughly twofold in many cases. Our initial experiments motivated a new compiler scheduling algorithm that is capable of tolerating the large and variable latencies that are common for disk accesses, in the presence of multiply-nested loops with unknown bounds. On our current experimental systems, many of our benchmark applications remain I/O bound, however, we show that the new scheduling algorithms are able to substantially improve performance in some cases, reducing execution time by an additional 36% in the best case. We further show that the new algorithms should enable applications to make more effective use of higher-bandwidth disk systems that will be available in the future.