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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Bibliographic Details
Published in:2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications pp. 56 - 62
Main Authors: Alonso, P., Dolz, M. F., Igual, F. D., Mayo, R., Quintana-Orti, E. S.
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2012
Subjects:
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
Online Access:Get full text
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Summary: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.
ISBN:1467316318
9781467316316
ISSN:2158-9178
DOI:10.1109/ISPA.2012.16