Name
Abstract | ||
|---|---|---|
This paper describes a novel approach to generate an optimized schedule to run threads on distributed shared memory (DSM) systems. The approach relies upon a binary instrumentation tool to automatically acquire the memory sharingrelationship between user-level threads by analyzing their memory trace. We introduce the concept of Affinity Graph to model the relationship. Expensive I/O for large trace files is completely eliminated by using an online graph creation scheme. We apply the technique of hierarchical graph partitioning and thread reordering to the affinity graph to determine an optimal thread schedule. We have performed experiments on an SGI Altix system. The experimental results show that our approach is able to reduce the totalexecution time by 10% to 38% for a variety of applications through the maximization of the data reuse within a single processor, minimization of the data sharing between processors, and a good load balance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1272366.1272380 | HPDC |
Keywords | Field | DocType |
optimized schedule,large trace file,novel approach,hierarchical graph partitioning,feedback-directed thread scheduling,affinity graph,memory consideration,optimal thread schedule,data reuse,online graph creation scheme,memory trace,memory sharingrelationship,graph partitioning,distributed shared memory,affinity,load balance,shared memory | Uniform memory access,Shared memory,Computer science,Parallel computing,Distributed memory,Real-time computing,Memory model,Memory management,Memory map,Distributed shared memory,Graph partition,Distributed computing | Conference |
Citations | PageRank | References |
10 | 0.77 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengguang Song | 1 | 232 | 19.88 |
| Shirley Moore | 2 | 342 | 33.61 |
| Jack J. Dongarra | 3 | 17625 | 2615.79 |