Computational frameworks for the fast Fourier transform
Computational frameworks for the fast Fourier transform
Real and complex fast Fourier transforms on the Fujitsu VPP 500
Parallel Computing
Fast matrix multiplies using graphics hardware
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Accelerator: using data parallelism to program GPUs for general-purpose uses
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
A memory model for scientific algorithms on graphics processors
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Memory Locality Exploitation Strategies for FFT on the CUDA Architecture
High Performance Computing for Computational Science - VECPAR 2008
Aspects of GPU for general purpose high performance computing
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
GPU acceleration of a production molecular docking code
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
A 32x32x32, spatially distributed 3D FFT in four microseconds on Anton
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Auto-tuning 3-D FFT library for CUDA GPUs
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
High-performance signal processing on emerging many-core architectures using CUDA
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Proceedings of the 24th ACM International Conference on Supercomputing
An empirically tuned 2D and 3D FFT library on CUDA GPU
Proceedings of the 24th ACM International Conference on Supercomputing
Large-scale FFT on GPU clusters
Proceedings of the 24th ACM International Conference on Supercomputing
Auto-tuning of fast fourier transform on graphics processors
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
fMRI analysis on the GPU-Possibilities and challenges
Computer Methods and Programs in Biomedicine
PARRAY: a unifying array representation for heterogeneous parallelism
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
High performance 3-D FFT using multiple CUDA GPUs
Proceedings of the 5th Annual Workshop on General Purpose Processing with Graphics Processing Units
True 4D image denoising on the GPU
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
Scalable multi-GPU 3-D FFT for TSUBAME 2.0 supercomputer
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
GPU optimization of convolution for large 3-d real images
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Optimizing tensor contraction expressions for hybrid CPU-GPU execution
Cluster Computing
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Most GPU performance "hypes" have focused around tightly-coupled applications with small memory bandwidth requirements e.g., N-body, but GPUs are also commodity vector machines sporting substantial memory bandwidth; however, effective programming methodologies thereof have been poorly studied. Our new 3-D FFT kernel, written in NVIDIA CUDA, achieves nearly 80 GFLOPS on a top-end GPU, being more than three times faster than any existing FFT implementations on GPUs including CUFFT. Careful programming techniques are employed to fully exploit modern GPU hardware characteristics while overcoming their limitations, including on-chip shared memory utilization, optimizing the number of threads and registers through appropriate localization, and avoiding low-speed stride memory accesses. Our kernel applied to real applications achieves orders of magnitude boost in power&cost vs. performance metrics. The off-card bandwidth limitation is still an issue, which could be alleviated somewhat with application kernels confinement within the card, while ideal solution being facilitation of faster GPU interfaces.