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The construction of phylogenetic trees is important for the computational biology, especially for the development of biological taxonomies. UPGMA is one of the most popular heuristic algorithms for constructing ultrametric trees (UT). Although the UT constructed by the UPGMA often is not a true tree unless the molecular clock assumption holds, the UT is still useful for the clocklike data. However,...
GPUs have been successfully used for acceleration of many mathematical functions and libraries. A common limitation of those libraries is a minimal size of primitives being handled in order to achieve significant speedups compared to their CPU versions. The minimal size requirement can prove prohibitive for many applications. It can be loosened by batching operations to have sufficient amount of data...
Sparse matrix vector multiplication, SpMV, is often a performance bottleneck in iterative solvers. Recently, Graphics Processing Units, GPUs, have been deployed to enhance the performance of this operation. We present a blocked version of the Transposed Jagged Diagonal storage format which is tailored for GPUs, BTJAD. We develop a highly optimized SpMV kernel that takes advantage of the properties...
Liquid chromatography-based tandem mass spectrometry (LC-MS) technique allows for identification and quantification of thousands of proteins in parallel. This technique coupled with a feed-forward artificial neural network provides a technique to analyze and select protein panels for use in multi-biomarker panel discovery applications. In this study, we enhance this technique by utilizing massively...
In this paper, we propose an efficient implementation of the branch and bound method for knapsack problems on a CPU-GPU system via CUDA. Branch and bound computations can be carried out either on the CPU or on a GPU according to the size of the branch and bound list. A better management of GPUs memories, less GPUCPU communications and better synchronization between GPU threads are proposed in this...
Unprecedented production of short reads from the new high-throughput sequencers has posed challenges to align short reads to reference genomes with high sensitivity and high speed. Many CPU-based short read aligners have been developed to address this challenge. Among them, one popular approach is the seed-and-extend heuristic. For this heuristic, the first and foremost step is to generate seeds between...
This paper elaborates on a new, fresh parallel optimization algorithm specially engineered to run on Graphic Processing Units (GPUs). The underlying operation relates to Systolic Computation. The algorithm, called Systolic Genetic Search (SGS) is based on the synchronous circulation of solutions through a grid of processing units and tries to profit from the parallel architecture of GPUs. The proposed...
Intensity model with blur effects are widely employed to accurately simulate the imaging process of a star simulator used for attitude determination and guiding feedback. The model is computationally intensive and the time requirements are proportional to the number of stars in the simulation, imposing great demands of computing power for realistic uses. This paper presents two star simulators using...
Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia "Fermi" class of GPGPUs. A new "padded jagged diagonals storage" (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the widespread ELLPACK-R scheme...
To cope with the complexity of programming GPU accelerators for medical imaging computations, we developed a framework to describe image processing kernels in a domain-specific language, which is embedded into C++. The description uses decoupled access/execute metadata, which allow the programmer to specify both execution constraints and memory access patterns of kernels. A source-to-source compiler...
Dynamic scheduling and varying decomposition granularity are well-known techniques for achieving high performance in parallel computing. Heterogeneous clusters with highly data-parallel processors, such as GPUs, present unique problems for the application of these techniques. These systems reveal a dichotomy between grain sizes: decompositions ideal for the CPUs may yield insufficient data-parallelism...
Approximate string matching using the k-mismatch technique has been widely applied to many fields such as virus detection and computational biology. The traditional parallel algorithms are all based on multiple processors, which have high costs of computing and communication. GPU has high parallel processing capability, low cost of computing, and less time of communication. To the best of our knowledge,...
The API interfaces provided by CUDA can help programmers develop CUDA applications and get high performance in GPU. However, many of the I/O operations are not supported in device codes. This paper has implemented most of the I/O functions through host's agent by using the characteristics of mapped memory in CUDA, such as read/write file and ¡®printf'. The methods that used to implement these I/O...
In the last few years, many scientific applications have been developed for powerful graphics processing units (GPUs) and have achieved remarkable speedups. This success can be partially attributed to high performance host callable GPU library routines that are offloaded to GPUs at runtime. These library routines are based on C/C++-like programming toolkits such as CUDA from NVIDIA and have the same...
Hybrid CPU/GPU computing architecture recently has become an alternative platform for high performance computing. This architecture provides massive computational power with lower energy consumption and less economic cost than the traditional one using only CPUs. However, the complexity of the GPU programming is too high for users to move their applications toward this hybrid computing architecture...
Graphics processing units (GPUs) are increasingly critical for general-purpose parallel processing performance. GPU hardware is composed of many streaming multiprocessors, each of which employs the single-instruction multiple-data (SIMD) execution style. This massively parallel architecture allows GPUs to execute tens of thousands of threads in parallel. Thus, GPU architectures efficiently execute...
As the demand for research on Image/ Content authentication has significantly increased, many authentication schemes have been proposed so far. But most of them are time consuming. This research concentrates on decreasing the time needed by an Image authentication algorithm. In this paper, we have shown a CUDA-based implementation of content authentication algorithm with NVIDIA's GeForce 8400 GS GPU...
Convolution is one of the most important operators used in image processing. With the constant need to increase the performance in high-end applications and the rise and popularity of parallel architectures, such as GPUs and the ones implemented in FPGAs, comes the necessity to compare these architectures in order to determine which of them performs better and in what scenario. In this article, convolution...
In this paper we present a fast unsupervised spoken term detection system based on lower-bound Dynamic Time Warping (DTW) search on Graphical Processing Units (GPUs). The lower-bound estimate and the K nearest neighbor DTW search are carefully designed to fit the GPU parallel computing architecture. In a spoken term detection task on the TIMIT corpus, a 55x speed-up is achieved compared to our previous...
Many geophysical problems are computationally expensive owing to their iterative nature or due to the programs processing to large datasets. Such problems are challenging and have to be approached with extreme caution because a wrong parameter selection will not only lead to wrong results but will also take up a lot of time. The Compute Unified Device Architecture (CUDA) introduced by NVIDIA has enabled...
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