The input/output complexity of sorting and related problems
Communications of the ACM
Scan primitives for vector computers
Proceedings of the 1990 ACM/IEEE conference on Supercomputing
Automatic Data Structure Selection and Transformation for Sparse Matrix Computations
IEEE Transactions on Parallel and Distributed Systems
Design issues for dynamic voltage scaling
ISLPED '00 Proceedings of the 2000 international symposium on Low power electronics and design
Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors
Segmented Operations for Sparse Matrix Computation on Vector Multiprocessors
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Understanding the efficiency of GPU algorithms for matrix-matrix multiplication
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Concurrent cache-oblivious b-trees
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Exploring Graphics Processor Performance for General Purpose Applications
DSD '05 Proceedings of the 8th Euromicro Conference on Digital System Design
A memory model for scientific algorithms on graphics processors
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Performance-Energy Tradeoffs for Matrix Multiplication on FPGA-Based Mixed-Mode Chip Multiprocessors
ISQED '07 Proceedings of the 8th International Symposium on Quality Electronic Design
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
Optimising data movement rates for parallel processing applications on graphics processors
PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
Exploring weak scalability for FEM calculations on a GPU-enhanced cluster
Parallel Computing
Studying Thermal Management for Graphics-Processor Architectures
ISPASS '05 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2005
Efficient gather and scatter operations on graphics processors
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Fast scan algorithms on graphics processors
Proceedings of the 22nd annual international conference on Supercomputing
On the energy efficiency of graphics processing units for scientific computing
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Analysis of Parallel Algorithms for Energy Conservation in Scalable Multicore Architectures
ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
An adaptive performance modeling tool for GPU architectures
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Towards optimizing energy costs of algorithms for shared memory architectures
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Energy-aware high performance computing with graphic processing units
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Bitonic sort on a chained-cubic tree interconnection network
Journal of Parallel and Distributed Computing
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With the continuous development of hardware and software, Graphics Processor Units (GPUs) have been used in the general-purpose computation field. They have emerged as a computational accelerator that dramatically reduces the application execution time with CPUs. To achieve high computing performance, a GPU typically includes hundreds of computing units. The high density of computing resource on a chip brings in high power consumption. Therefore power consumption has become one of the most important problems for the development of GPUs. This paper analyzes the energy consumption of parallel algorithms executed in GPUs and provides a method to evaluate the energy scalability for parallel algorithms. Then the parallel prefix sum is analyzed to illustrate the method for the energy conservation, and the energy scalability is experimentally evaluated using Sparse Matrix-Vector Multiply (SpMV). The results show that the optimal number of blocks, memory choice and task scheduling are the important keys to balance the performance and the energy consumption of GPUs.