In order to achieve greater amounts of computation while lowering the cost of artistic and scientific projects that rely on realtime digital signal processing techniques, it is interesting to study the performance of commodity parallel processing GPU cards coupled with commonly used software for realtime DSP. In this article, we describe the measurement of data roundtrip time using the Pure Dat...

GPU devices typically have a higher off-chip bandwidth than FPGA-based systems. Thus typically GPU should perform better for bandwidth-bounded massive parallel applications. In this paper we present our implementations of a 3D recursive Gaussian IIR on multicore CPU, many-core GPU and multi-FPGA platforms. Our baseline implementation on the CPU features the smallest arithmetic computation (2 MA...

Graphics Processing Units (GPUs) are massive data parallel processors. High performance comes only at the cost of identifying data parallelism in the applications while using data parallel processors like GPU. This is an easy effort for applications that have regular memory access and high computation intensity. GPUs are equally attractive for sparse matrix vector multiplications (SPMV for shor...

Graphics Processing Units (GPU) have significantly more applications than just rendering images. They are also used in general-purpose computing to solve problems that can benefit from massive parallel processing. However, there are tasks that either hardly suit GPU or fit GPU only partially. The latter class is the focus of this paper. We elaborate on hybrid CPU/GPU computation and build optim...

The increasing complexity of modern AC/DC power systems poses a significant challenge to fast solution large-scale transient stability simulation problems. This paper proposes the hybrid parallel-in-time-and-space (PiT+PiS) on CPU-GPU platform thoroughly exploit parallelism from time and spatial perspectives, thereby fully utilizing parallel processing hardware. respective electromechanical ele...

We propose a four-tiered parallelization model for acceleration of the secure multiparty computation (SMC) on the CUDA based Graphic Processing Unit (GPU) cluster architecture. Specification layer is the top layer, which adopts the SFDL of Fairplay for specification of secure computations. The SHDL file generated by the SFDL compiler of Fairplay is used as inputs to the function layer, for whic...

We are interested in the intensive use of Factorial Correspondence Analysis (FCA) for large-scale content-based image retrieval. Factorial Correspondence Analysis, is a useful method for analyzing textual data, and we adapt it to images using the SIFT local descriptors. FCA is used to reduce dimensions and to limit the number of images to be considered during the search. Graphics Processing Uni...

Spatial access methods (SAMs) are used for information retrieval in large spatial databases. Many of the SAMs use sequential tree structures to search the result set of the spatial data which are contained in the given query region. In order to improve performance for the SAM, this paper proposes a parallel method using GPU. Since the searching process needs intensive computation but is indepen...

We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode m...

MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13× faster than the serial CPU version of the ali...