Accelerating advanced mri reconstructions on gpus
Proceedings of the 5th conference on Computing frontiers
Accelerating advanced MRI reconstructions on GPUs
Journal of Parallel and Distributed Computing
Data parallel acceleration of decision support queries using Cell/BE and GPUs
Proceedings of the 6th ACM conference on Computing frontiers
A translation system for enabling data mining applications on GPUs
Proceedings of the 23rd international conference on Supercomputing
Analysis of Parallel Algorithms for Energy Conservation with GPU
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Initial experiences porting a bioinformatics application to a graphics processor
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
RACECAR: a heuristic for automatic function specialization on multi-core heterogeneous systems
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
The RACECAR heuristic for automatic function specialization on multi-core heterogeneous systems
Proceedings of the 2012 international conference on Compilers, architectures and synthesis for embedded systems
Energy cost evaluation of parallel algorithms for multiprocessor systems
Cluster Computing
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Graphics processors are designed to perform many floating-point operations per second. Consequently, they are an attractive architecture for high-performance computing at a low cost. Nevertheless, it is still not very clear how to exploit all their potential for general-purpose applications. In this work we present a comprehensive study of the performance of an application executing on the GPU. In addition, we analyze the possibility of using the graphics card to extend the life-time of a computer system. In our experiments we compare the execution on a midclass GPU (NVIDIA GeForce FX 5700LE) with a high-end CPU (Pentium 4 3.2GHz). The results show that to achieve high speedup with the GPU you need to: (1) format the vectors into two-dimensional arrays; (2) process large data arrays; and (3) perform a considerable amount of operations per data element. Finally, we study the performance when upgrading a low-end system by simply adding a GPU. This solution is cheaper, results in smaller power consumption and achieves higher speedup (8.1x versus 1.3x) than a full upgrade to a new high-end system.