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It is an important task to tune performance for sparse matrix vector multiplication (SpMV), but it is also a difficult task because of its irregularity. In this paper, we propose a cache blocking method to improve the performance of SpMV on the emerging GPU architecture. The sparse matrix is partitioned into many sub-blocks, which are stored in CSR format. With the blocking method, the corresponding...
This paper presents a method to map and implement the 1-D FFT on a GPGPU and extends the method to the 2-D FFT. Two approaches are used to maximize the performance. One is to localize data inside the caches of the GPGPU and the other is to properly assign threads and blocks to reach higher performance. The results show that our implementation is 3.62 times faster to perform 32M-point 1-D FFT and 4...
As graphics processing units (GPUs) gain adoption as general purpose parallel compute devices, several key problems need to be addressed in order for their use to become more practical and more user friendly. One such problem is special functions designed to execute on GPUs called kernel functions are non-preempt able. Once the kernel is issued to the GPU it will remain there till either execution...
Graphics Processing Units (GPU) are becoming increasingly popular in high performance computing due to their high performance, high power efficiency and low cost. In this paper, we present results of an effort to implement the fatlink computation -- an important component of many lattice quantum chromo dynamics (LQCD) calculations -- on GPU clusters using the QUDA framework. Two implementations, one...
The element subroutines in finite element method (FEM) provides enough parallelism to be successfully accelerated by contemporary GPUs. However, their efficient implementation is not straightforward and requires time-consuming exploration of numerous implementation variants. In this paper, we present kernel fusion as an optimization technique and its application for element subroutines. Moreover,...
With the rising number of application accelerators, developers are looking for ways to evaluate new and competing platforms quickly, fairly, and early in the development cycle. As high-performance computing (HPC) applications increase their demands on application acceleration platforms, graphics processing units (GPUs) provide a potential solution for many developers looking for increased performance...
The quality of an image is highly critical for applications such as robotic vision, surveillance, medical imaging, etc. The images captured in real-time are seldom noise free and therefore require noise removal for further processing. Out of several proposed noise removal schemes, an isotropic diffusion filtering is known to achieve highly precise results. However, the accuracy comes at an expense...
General-purpose graphics processing units (GPUs) have been found to be viable solutions for large-scale numerical computations with an inherent potential for massive parallelism. In contrast, only few is known about using GPUs for small-scale computations. To have the GPU not be under-utilized for small problem sizes, a meaningful approach is to perform as many small-scale computations as possible...
Energy and power density concerns in modern processors have led to significant computer architecture research efforts in power-aware and temperature-aware computing. With power dissipation becoming an increasingly vexing problem, power analysis of Graphical Processing Unit (GPU) and its components has become crucial for hardware and software system design. Here, we describe our technique for a coordinated...
In this paper, we first discussed the video decoding standard and its architecture, and then analyzed the decoding complexity of each process. By using the benefit of the CUDA programming model, and taking advantages of GPU to optimize the decoding process of MC (motion compensation) and CSC(color space conversion) that are very time consuming, we proposed a MC accelerating method based on CUDA, and...
Architectures in which multicore chips are augmented with graphics processing units (GPUs) have great potential in many domains in which computationally intensive real-time workloads must be supported. However, unlike standard CPUs, GPUs are treated as I/O devices and require the use of interrupts to facilitate communication with CPUs. Given their disruptive nature, interrupts must be dealt with carefully...
Graphics processors were originally developed for rendering graphics but have recently evolved towards being an architecture for general-purpose computations. They are also expected to become important parts of embedded systems hardware -- not just for graphics. However, this necessitates the development of appropriate timing analysis techniques which would be required because techniques developed...
GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle of a low priority job execution or memory copy...
Many general-purpose applications exploit Graphics Processing Units (GPUs) by executing a set of well-known dataparallel primitives. Those primitives are usually invoked from the host many times, so their throughput has a great impact on the performance of the overall system. Thus, the study of novel algorithmic strategies to optimize their implementation on current devices is an interesting topic...
Image demosaicing algorithms are used to reconstruct a full color image from the incomplete color samples output (RAW data) of an image sensor overlaid with a Color Filter Array (CFA). Better demosaicing algorithms are superior in terms of acuity, dynamic range, signal to noise ratio, and artifact suppression, which make them suitable for high quality delivery such as theatrical broadcast. In this...
This paper presents an integrated analytical and profile-based CUDA performance modeling approach to accurately predict the kernel execution times of sparse matrix-vector multiplication for CSR, ELL, COO, and HYB SpMV CUDA kernels. Based on our experiments conducted on a collection of 8 widely-used testing matrices on NVIDIA Tesla C2050, the execution times predicted by our model match the measured...
Stochastic simulations are often sensitive to the source of randomness that characterizes the statistical quality of their results. Consequently, we need highly reliable Random Number Generators (RNGs) to feed such applications. Recent developments try to shrink the computation time by relying more and more General Purpose Graphics Processing Units (GPGPUs) to speedup stochastic simulations. Such...
QUAD stream cipher uses multivariate polynomial systems. It has provable security based on the computational hardness assumption. More specifically, the security of QUAD depends on hardness of solving non-linear multivariate system us over a finite field, and it is known as an NP-Hard problem. However, QUAD is slower than other stream ciphers, and an efficient implementation, which has a reduced computational...
Data clustering is a distinctive method for analyzing complex networks in terms of functional relationships of the comprising elements. A number of graph-based algorithms have been proposed so far to tackle the complexity of the problem and many of them are based on the representation of data in the form of a minimum spanning tree (MST). In this work, we propose a graph-based agglomerative clustering...
Using GPUs as computational accelerators has been a growing area of research in the past several years. One particular area amenable to exploiting video card hardware is dense linear algebra. We continue this trend by generalizing the MAGMA xGEMM kernels, porting them to OpenCL and tuning them to run on the AMD 7970. Achieving up to 1.7 TFlops in SGEMM and 650 GFlops in DGEMM, we extend this performance...
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