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Next: Prefetching background Up: Automatic I/O Hint Generation Previous: Automatic I/O Hint Generation


Many applications, ranging from simple text search utilities to complex databases, issue large numbers of file access requests that cannot always be serviced by in-memory caches. Due to the disparity between processor speeds and disk access times, the execution times of these applications are often dominated by I/O latency. Furthermore, since disk access times are improving only slowly, these applications are receiving decreasing benefits from the rapid advance of processor technology, and I/O latency is accounting for an increasing proportion of their execution times.

File systems can automatically hide disk latency during file writes by performing write-behind buffering [Powell77], in which they inform the application that the write request has completed before propagating the data to disk. Automatically hiding the disk latency of file reads is more complicated since, in most applications, the requested data is used as soon as the read returns. Prefetching, requesting data before it is needed in order to move it from a high-latency locale (e.g. disk) to a low-latency locale (e.g. memory), is a well-known technique for hiding read latency. To be effective, prefetching requires that the I/O system provide more bandwidth than the application already consumes. Fortunately, we can construct cost-efficient I/O systems capable of providing adequate bandwidth by striping data across an array of disks [Patterson88] or, to facilitate sharing of I/O resources, across multiple higher-level entities like file servers or network disks [Cabrera91, Hartman94, Gibson98].

The difficulty with prefetching lies in knowing how to accurately determine what and when to prefetch. Prefetching consumes processor, cache and I/O resources; if unneeded data is prefetched, or data is prefetched prematurely, I/O requests for more immediately needed data may be delayed and/or more immediately needed data may be displaced from the file cache. One effective alternative is to manually modify applications so that they explicitly control I/O prefetching. Unfortunately, as we will discuss in the next section, this can be a difficult optimization problem for the programmer. Automatic prefetching, however, can significantly reduce execution time without increasing programming effort, provided that the automatic methods are sufficiently accurate, timely and careful with resource usage. In this paper, we present a novel approach to automatic prefetching that is potentially applicable to virtually all disk-bound applications and should be much more effective than existing automatic approaches for disk-bound applications with irregular and input-dependent access patterns.

Our approach arises from the observation that the cycles during which an application is stalled waiting for the I/O system to service a read request are often wasted. This situation occurs commonly both in desktop computing environments and where disk-bound applications are important enough to acquire exclusive use of a high-performance server machine. Even high-performance disk systems currently have at least 10 millisecond access latencies, so that processors may be wasting millions of cycles during each I/O stall. We propose that a wide range of disk-bound applications can use these cycles to dynamically discover their own future read accesses by performing speculative execution, a possibly erroneous pre-execution of their code.

We present a design for automatically transforming applications to perform speculative execution and issue hints for their future read accesses. Our design takes advantage of TIP [Patterson95], an informed prefetching and caching manager that uses application-generated hints to better exploit the file cache and I/O resources. We have implemented a binary modification tool, SpecHint, that performs this transformation. Using SpecHint, we obtain substantial reductions (29%, 69% and 70%) in the execution times of three real-world applications from the TIP benchmark suite [Patterson95] when the data is striped over four disks. For two of the three applications, we automatically obtain the same benefit as was obtained by manually modifying the applications to issue hints. We examine the performance of our design in a variety of configurations, explaining the circumstances under which it falls short of the performance achieved by manually-hinted prefetching. Through simulation, we also estimate how the performance of our design will be affected by the widening gap between processor and disk speeds.

This paper is organized as follows. In Section 2, we discuss previous prefetching mechanisms. In Section 3, we present our new automatic approach and our design for transforming applications. In Section 4, we describe our experimental framework and results. Finally, in Sections 5, 6, and 7, we present future work, related work, and conclusions.

next up previous
Next: Prefetching background Up: Automatic I/O Hint Generation Previous: Automatic I/O Hint Generation

Fay Chang
Tue Jan 5 18:05:04 EST 1999