Aggressive prefetching is an effective technique for reducing the execution times of disk-bound applications; that is, applications that manipulate data too large or too infrequently used to be found in file or disk caches. While automatic prefetching approaches based on static analysis or historical access patterns are effective for some workloads, they are not as effective as manually-driven (programmer-inserted) prefetching for applications with irregular or input-dependent access patterns. In this paper, we propose to exploit whatever processor cycles are left idle while an application is stalled on I/O by using these cycles to dynamically analyze the application and predict its future I/O accesses. Our approach is to speculatively pre-execute the application’s code in order to discover and issue hints for its future read accesses. Coupled with an

The Network File System (NFS) utilizes a stateless protocol between clients and servers; the major advantage of this statelessness is that NFS crash recovery is very easy. However, the protocol requires that data modification operations such as write be fully committed to stable storage before replying to the client. The cost of this is significant in terms of response latency and server CPU and I/O loading. This paper describes a write gathering technique that exploits the fact that there are often several write requests for the same file presented to the server at about the same time. With this technique the data portions of these writes are combined and a single metadata update is done that applies to them all. No replies are sent to the client until after this metadata update has been fully committed, thus the NFS crash recovery design is not violated. This technique can be used in most NFS server implementations and requires no client modifications. 1. Introduction The...

Aggressive prefetching is an effective technique for reducing the execution times of disk-bound applications; that is, applications that manipulate data too large or too infrequently used to be found in file or disk caches. While automatic prefetching approaches based on static analysis or historical access patterns are effective for some workloads, they are not as effective as manually-driven (programmer-inserted) prefetching for applications with irregular or input-dependent access patterns. In this paper, we propose to exploit whatever processor cycles are left idle while an application is stalled on I/O by using these cycles to dynamically analyze the application and predict its future I/O accesses. Our approach is to speculatively pre-execute the application's code in order to discover and issue hints for its future read accesses. Coupled with an aggressive hint-driven prefetching system, this automatic approach could be applied to arbitrary applications, and should be particularl...