Type of Document Dissertation Author Blagojevic, Filip URN etd-06052008-012156 Title Scheduling on Asymmetric Architectures Degree PhD Department Computer Science Advisory Committee
Advisor Name Title Nikolopoulos, Dimitrios S. Committee Chair Cameron, Kirk W. Committee Member Feng, Wu-Chun Committee Member Lowenthal, David K. Committee Member Ribbens, Calvin J. Committee Member Keywords
- process scheduling
- performance prediction
- high-performance computing
- runtime adaptation
- Multicore processors
- Cell BE
Date of Defense 2008-05-30 Availability unrestricted AbstractWe explore runtime mechanisms and policies for scheduling dynamic multi-grain parallelism on heterogeneous multi-core processors. Heterogeneous multi-core processors integrate conventional cores that run legacy codes with specialized cores that serve as computational accelerators. The term multi-grain parallelism refers to the exposure of multiple dimensions of parallelism from within the runtime system, so as to best exploit a parallel architecture with heterogeneous computational capabilities
between its cores and execution units. To maximize performance on
heterogeneous multi-core processors, programs need to expose multiple dimensions of parallelism simultaneously. Unfortunately, programming with multiple dimensions of parallelism is to date an ad hoc process, relying heavily on the intuition and skill of programmers. Formal techniques are needed to optimize multi-dimensional parallel program designs. We investigate user- and kernel-level schedulers that dynamically "rightsize" the dimensions and degrees of parallelism on the asymmetric parallel platforms. The schedulers address the problem of mapping application-specific concurrency to an architecture with multiple hardware layers of parallelism, without requiring programmer intervention or sophisticated compiler support. Our runtime environment outperforms the native Linux and MPI scheduling environment by up to a factor of 2.7. We also present a model of multi-dimensional parallel computation for steering the parallelization process on heterogeneous multi-core processors. The model predicts with high accuracy the execution time and
scalability of a program using conventional processors and
accelerators simultaneously. More specifically, the model reveals optimal degrees of multi-dimensional, task-level and data-level concurrency, to maximize performance across cores. We evaluate our runtime policies as well as the performance model we developed, on an IBM Cell BladeCenter, as well as on a cluster composed of Playstation3 nodes, using two realistic bioinformatics applications.
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