Name
Abstract | ||
|---|---|---|
We present a novel, linear programming (LP)-based scheduling algorithm that exploits heterogeneous multicore architectures such as CPUs and GPUs to accelerate a wide variety of proximity queries. To represent complicated performance relationships between heterogeneous architectures and different computations of proximity queries, we propose a simple, yet accurate model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. Since our method is general, we are able to apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. Unlike prior scheduling methods, our method continually improves the performance, as we add more computing resources. Also, our method achieves much higher performance improvement compared with prior methods as heterogeneity of computing resources is increased. Moreover, for one of tested applications, our method achieves even higher performance than a prior parallel method optimized manually for the application. We also show that our method provides results that are close (e.g., 75 percent) to the performance provided by a conservative upper bound of the ideal throughput. These results demonstrate the efficiency and robustness of our algorithm that have not been achieved by prior methods. In addition, we integrate one of our contributions with a work stealing method. Our version of the work stealing method achieves 18 percent performance improvement on average over the original work stealing method. This result shows wide applicability of our approach. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TVCG.2013.71 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
optimisation,different gpus,motion planning,heterogeneous architectures,proximity queries,prior method,gpu,scheduling,prior parallel method,proximity query,ray tracing,collision detection,graphics processing units,higher performance improvement,heterogeneous system,magnitude performance improvement,prior scheduling method,heterogeneous computing environments,linear programming,lp based scheduling algorithm,multiprocessing systems,optimization problem,cpu,multicore architectures,percent performance improvement,computer architecture,work stealing method,complicated performance relationship,computing resources,query processing,higher performance,computational modeling,multicore processing,acceleration,scheduling algorithms,optimization | Computer science,Scheduling (computing),Parallel computing,Symmetric multiprocessor system,Robustness (computer science),Work stealing,Linear programming,Multi-core processor,Optimization problem,Performance improvement | Journal |
Volume | Issue | ISSN |
19 | 9 | 1941-0506 |
Citations | PageRank | References |
3 | 0.41 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duksu Kim | 1 | 66 | 5.00 |
| JinKyu Lee | 2 | 615 | 54.04 |
| Junghwan Lee | 3 | 50 | 12.51 |
| Insik Shin | 4 | 1208 | 64.24 |
| john kim | 5 | 1412 | 72.87 |
| Sung-Eui Yoon | 6 | 798 | 54.82 |