Title page for ETD etd-06052008-012156

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
  • process scheduling
  • performance prediction
  • high-performance computing
  • runtime adaptation
  • Multicore processors
  • Cell BE
Date of Defense 2008-05-30
Availability unrestricted
We 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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