Phenomenal improvements in the computational performance of multiprocessors have not been matched by comparable gains in I/O system performance. This imbalance has resulted in I/O becoming a signi cant bottleneck for many scienti c applications. One key to overcoming this bottleneck is improving the performance of parallel le systems. The design of a high-performance parallel le system requires a comprehensive understanding of the expected workload. Unfortunately, u n til recently, no general workload studies of parallel le systems have been conducted. The goal of the CHARISMA project was to remedy this problem by c haracterizing the behavior of several production workloads, on di erent machines, at the level of individual reads and writes. The rst set of results from the CHARISMA project describe the workloads observed on an Intel iPSC/860 and a Thinking Machines CM-5. This paper is intended to compare and contrast these two w orkloads for an understanding of their essential similarities and di erences, isolating common trends and platform-dependent v ariances. Using this comparison, we are able to gain more insight i n to the general principles that should guide parallel le-system design.
Multiprocessors have permitted astounding increases in computational performance, but many cannot meet the intense I/O requirements of some scienti c applications. An important component of any solution to this I/O bottleneck is a parallel le system that can provide high-bandwidth access to tremendous amounts of data in parallel to hundreds or thousands of processors.Most successful systems are b ased on a solid understanding of the expected workload, but thus far there have been no comprehensive workload characterizations of multiprocessor le systems. This paper presents the results of a three w e ek tracing study in which all le-related activity on a massively parallel computer was recorded. Our instrumentation di ers from previous e orts in that it collects information about every I/O request and about the mix of jobs running in a production environment. We also present the results of a trace-driven caching simulation and recommendations for designers of multiprocessor le systems.
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