Name
Abstract | ||
|---|---|---|
Modulo scheduling is an effective instruction scheduling technique for exploiting ILP in modern microprocessors with wide machine resources. As with any instruction scheduling technique, its effectiveness is constrained by both the source program dependencies as deduced by the compiler and the resources available in the target machine. Data speculation is a technique for eliminating memory dependencies in the instruction dependence graph with demonstrated benefits in acyclic scheduling. Therefore, by applying data speculation in modulo scheduled loops we are able to achieve higher degrees of instruction level parallelism. This paper addresses a number of key issues in applying data speculation to modulo scheduling: effectively identifying the additional instructions required to recover from mis-speculation, ensuring full recoverability of instructions speculated across multiple stages and possibly executed speculatively multiple times on-the-fly, and coloring rotating registers for speculated instructions and instructions in recovery blocks. We implemented the framework proposed in this paper and demonstrated its effectiveness through experimental results. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/PACT.2000.888341 | IEEE PACT |
Keywords | Field | DocType |
speculatively multiple times on-the-fly,modulo scheduling,data speculation,additional instruction,effective instruction scheduling technique,multiple stage,instruction level parallelism,instruction dependence graph,modulo scheduled loops,acyclic scheduling,instruction scheduling technique,instruction scheduling,computer architecture,collaborative software,production,hardware,compiler,scheduling algorithm | Speculation,Instruction-level parallelism,Graph,Instruction scheduling,Scheduling (computing),Computer science,Modulo,Collaborative software,Parallel computing,Compiler,Real-time computing | Conference |
ISBN | Citations | PageRank |
0-7695-0622-4 | 6 | 0.75 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Uma Mahadevan | 1 | 90 | 7.78 |
| Kevin Nomura | 2 | 23 | 1.72 |
| Roy Dz-ching Ju | 3 | 326 | 21.37 |
| Rick Hank | 4 | 6 | 0.75 |