Reducing Energy Consumption of Dense Linear Algebra Operations on Hybrid CPU-GPU Platforms

We investigate the balance between the time-to-solution and the energy consumption of a task-parallel execution of the Cholesky and LU factorizations on a hybrid platform, equipped with a multi-core processor and several GPUs. To improve energy efficiency, we incorporate two energy-saving techniques...

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Veröffentlicht in:2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications S. 56 - 62
Hauptverfasser: Alonso, P., Dolz, M. F., Igual, F. D., Mayo, R., Quintana-Orti, E. S.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2012
Schlagworte:
dense linear algebra
Energy consumption
Energy-aware algorithms
Graphics processing unit
graphics processors
high performance computing
Kernel
Linear algebra
multi-core processors
Multicore processing
Runtime
ISBN:1467316318, 9781467316316
ISSN:2158-9178
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Abstract We investigate the balance between the time-to-solution and the energy consumption of a task-parallel execution of the Cholesky and LU factorizations on a hybrid platform, equipped with a multi-core processor and several GPUs. To improve energy efficiency, we incorporate two energy-saving techniques in the runtime in charge of scheduling the computations, to block idle threads and enable the transition to a more energy-friendly state of the general-purpose cores. Experiments on an Intel Xeon-based platform connected to an NVIDIA Tesla server report an average reduction of the energy consumption close to 9% (38% when only the consumption associated with the application is considered), for a minor increase in the execution time of the algorithm.
AbstractList We investigate the balance between the time-to-solution and the energy consumption of a task-parallel execution of the Cholesky and LU factorizations on a hybrid platform, equipped with a multi-core processor and several GPUs. To improve energy efficiency, we incorporate two energy-saving techniques in the runtime in charge of scheduling the computations, to block idle threads and enable the transition to a more energy-friendly state of the general-purpose cores. Experiments on an Intel Xeon-based platform connected to an NVIDIA Tesla server report an average reduction of the energy consumption close to 9% (38% when only the consumption associated with the application is considered), for a minor increase in the execution time of the algorithm.
Author Quintana-Orti, E. S.
Mayo, R.
Alonso, P.
Igual, F. D.
Dolz, M. F.
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  givenname: M. F.
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  surname: Quintana-Orti
  fullname: Quintana-Orti, E. S.
  email: quintana@icc.uji.es
  organization: Depto. de Ing. y Cienc. de Comput., Univ. Jaume I, Castellon, Spain
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Snippet We investigate the balance between the time-to-solution and the energy consumption of a task-parallel execution of the Cholesky and LU factorizations on a...
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StartPage 56
SubjectTerms dense linear algebra
Energy consumption
Energy-aware algorithms
Graphics processing unit
graphics processors
high performance computing
Kernel
Linear algebra
multi-core processors
Multicore processing
Runtime
Title Reducing Energy Consumption of Dense Linear Algebra Operations on Hybrid CPU-GPU Platforms
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