Name
Abstract | ||
|---|---|---|
Uniformly distributing parallel workloads amongst threads is an effective strategy for programmers to increase application performance. However, in any parallel segment, execution time is determined by the longest running thread. Even for embarrassingly parallel programs in the form of SPMD (single program multiple data), the threads are not perfectly balanced due to control flow divergence, non-deterministic memory latencies, and synchronization operations. Such an imbalance can be significantly exacerbated by performance asymmetry among cores, which is likely to exist in future generations of chip multiprocessors (CMPs) either for energy efficiency or due to process variation. We propose Dynamic Core Boosting (DCB), a software-hardware cooperative system that mitigates the workload imbalance problem in performance asymmetric CMPs. Relying on dynamic voltage and frequency scaling to accelerate individual cores at a fine granularity, DCB attempts to balance the workloads by detecting and boosting critical threads. DCB coordinates its compiler and runtime to enable asymmetric CMPs to achieve near-optimal utilization of core boosting. The compiler instruments the program with instructions to give progress hints and the runtime monitors their execution, enabling DCB to intelligently accelerate selected threads within a total core boosting budget for better performance. On a simulated eight core system of varying frequency, our experiments using PARSEC benchmarks show that DCB improves the overall performance by an average of 33%, outperforming a reactive boosting scheme by an average of 10%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2597917.2597932 | Conf. Computing Frontiers |
Keywords | Field | DocType |
algorithms,design,workload balancing,experimentation,core boosting,critical path acceleration,optimization,performance | SPMD,Parsec,Computer science,Control flow,Parallel computing,Embarrassingly parallel,Thread (computing),Real-time computing,Compiler,Boosting (machine learning),Frequency scaling | Conference |
Citations | PageRank | References |
2 | 0.36 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyoun Kyu Cho | 1 | 52 | 4.37 |
| Scott Mahlke | 2 | 4811 | 312.08 |