Name
Abstract | ||
|---|---|---|
One of the most important factors that determine performance of data-intensive applications is data locality. A program with high data locality makes better use of fast, on-chip memories and can avoid large main memory latencies. Although previous compiler research investigated numerous techniques for enhancing locality, we lack of formal techniques, against which the existing heuristics can be compared. Motivated by this observation, this paper presents a fresh look at locality optimization based on integer linear programming (ILP). We formulate the conditions for data locality, and present a system of constraints whose solution gives optimal computation re-ordering and data-to-memory assignment under our objective function and cost model. Our experimental results using three data-intensive applications clearly indicate that the ILP-based approach generates very good results and outperforms a previously proposed heuristic solution to locality. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/11532378_12 | LCPC |
Keywords | Field | DocType |
data locality,better use,heuristic solution,data-intensive application,data-to-memory assignment,locality optimization,high data locality,existing heuristics,cost model,ilp-based approach,objective function,memory latency,chip | Locality,Heuristic,Computer science,CPU cache,Parallel computing,Compiler,Integer programming,Heuristics,Linear programming,Formal methods | Conference |
Volume | ISSN | ISBN |
3602 | 0302-9743 | 3-540-28009-X |
Citations | PageRank | References |
1 | 0.36 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guilin Chen | 1 | 92 | 10.54 |
| Ozcan Ozturk | 2 | 112 | 15.25 |
| Mahmut T. Kandemir | 3 | 7371 | 568.54 |