Name
Abstract | ||
|---|---|---|
Irregular computations over large-scale sparse data are prevalent in critical data applications and they have significant room for improvement on modern computer systems from the aspects of parallelism and data locality. We introduce new techniques to efficiently map large irregular computations with multi-dimensional sparse arrays (or sparse tensors) onto modern multi-core systems with non-uniform memory access (NUMA) behavior. We implement a static-cum-dynamic task scheduling scheme with low overhead for effective parallelization of sparse computations. We introduce locality-aware optimizations to the task scheduling mechanism that are driven by the sparse input data pattern. We evaluate our techniques on key sparse tensor decomposition methods that are widely used in areas such as data mining, graph analysis, and elsewhere. We achieve around 4-5x improvement in performance over existing parallel approaches and observe scalable parallel performance on modern multi-core systems with up to 32 processor cores. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2597652.2600115 | I4CS |
Keywords | Field | DocType |
compilers,sparse tensors,optimization,irregular computations,parallelization | Locality,Scheduling (computing),Computer science,Parallel computing,Sparse approximation,Power graph analysis,Multi-core processor,Sparse matrix,Computation,Scalability | Conference |
Citations | PageRank | References |
5 | 0.51 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muthu Manikandan Baskaran | 1 | 493 | 33.10 |
| Benoît Meister | 2 | 138 | 12.84 |
| Richard Lethin | 3 | 118 | 17.17 |