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Convolution is a fundamental operation in many applications, such as computer vision, natural language processing, image processing, etc. Recent successes of convolutional neural networks in various deep learning applications put even higher demand on fast convolution. The high computation throughput and memory bandwidth of graphics processing units (GPUs) make GPUs a natural choice for accelerating...
Image classification is one the important processing done on satellite images. Many algorithm are proposed for such classification of which Support Vector Machine (SVM) is mostly used. Many variants and approaches of SVM are proposed of which GA based classifiers shows better prospects. But increasing size, spectrum and multiple dimension of remote sensing data has made image processing problem more...
Support vector machine (SVM) is a popular classifier dealing with small-scale datasets. It has outstanding performance compared to other classifiers. However the execution time is extremely long when training Big Data. The Graphics Processing Unit (GPU) is a massively parallel device which performs very well as a co-processor. NVIDIA proposed a programming platform, CUDA, in 2006, which makes it much...
Parallel and distributed systems that support the shared memory paradigm are becoming widely accepted in many areas of computing. The memory consistency model of a shared-memory multiprocessor system influences both the performance and the programmability of the system. Under optimal condition it is found that multithreading contributes to more than 50 percent of performance improvement, while the...
In this paper, four beamforming algorithms (i.e., interpolation and phase rotation with pre- and post-filtering, IBF-PRE, IBF-POST, PRBF-PRE and PRBF-POST, respectively) implemented on a high-performance graphics-processing unit (GPU) were presented. Each beamforming method was divided into two kernels consisting of various beamforming and mid-processing blocks and efficiently implemented on a NVIDIA's...
Lack of efficient and transparent interaction with GPU data in hybrid MPI+GPU environments challenges GPU acceleration of large-scale scientific computations. A particular challenge is the transfer of noncontiguous data to and from GPU memory. MPI implementations currently do not provide an efficient means of utilizing data types for noncontiguous communication of data in GPU memory. To address this...
Many core accelerators are being deployed in many systems to improve the processing capabilities. In such systems, application mapping need to be enhanced to maximize the utilization of the underlying architecture. Especially in GPUs mapping becomes critical for multi-kernel applications as kernels may exhibit different characteristics. While some of the kernels run faster on GPU, others may refer...
In cutting-edge CPU/GPU hybrid clusters, such as Tianhe-1A, the aggregate CPU computing capability may amount to up to 1/3 of the aggregate GPU computing capability. It thus goes without saying that the CPUs and GPUs should jointly carry out the computational work. However, to effectively and simultaneously use both the hardware components requires great care when developing the parallel implementations...
One of the stages of the analysis of satellite images is given by a classification based on the Markov Random Fields (MRF) method. It is possible to find in literature several packages to carry out this analysis, and of course the classification tasks. One of them is the Orfeo Tool Box (OTB). The analysis of satellite images is an expensive computational task requiring real time execution or automatization...
Current generation of multicore computing platforms are vastly different. Sustenance of many core applications across heterogenous platforms is a daunting task, more so when dynamic nature of the application is factored in. Open Computing Language (OpenCL) was created to address this issue. Designed to run on CPUs, GPUs, FPGAs and other platforms. OpenCL is becoming a standard for cross-platform parallel...
Transition to hybrid CPU/GPU platforms in high performance computing is challenging in the aspect of efficient utilisation of the heterogeneous hardware and existing optimised software. During recent years, scientific software has been ported to multicore and GPU architectures and now should be reused on hybrid platforms. In this paper, we model the performance of such scientific applications in order...
More and more computationally intensive scientific applications make use of hardware accelerators like general purpose graphics processing units (GPGPUs). Compared to software development for typical multi-core processors their programming is fairly complex and needs hardware specific optimizations to utilize the full computing power. To achieve high performance, critical parts of a program have to...
The use of accelerators in high-performance computing is increasing. The most commonly used accelerator is the graphics processing unit (GPU) because of its low cost and massively parallel performance. The two most common programming environments for GPU accelerators are CUDA and OpenCL. While CUDA runs natively only on NVIDIA GPUs, OpenCL is an open standard that can run on a variety of hardware...
CUDA performs general purpose parallel computing using GPGPU, which has been applied to various computing fields. However, the multi-address-space architecture in CUDA makes memory management complicated. NVIDIA introduced UVA, Unified Virtual Addressing, into CUDA Toolkit 4.0 to address this issue. However, UVA has platform limitations and even performance loss under certain circumstances. We propose...
The goal of face detection is to determine the presence of faces in arbitrary images, along with their locations and dimensions. As it happens with any graphics workloads, these algorithms benefit from data-level parallelism. Existing parallelization efforts strictly focus on mapping different divide and conquer strategies into multicore CPUs and GPUs. However, even the most advanced single-chip many-core...
Given the extraordinary computational power of modern graphics processing units (GPUs), general purpose computation on GPUs (GPGPU) has become an increasingly important platform for high performance computing. To better understand how well the GPU resource has been utilized by application developers and then to facilitate them to develop high performance GPGPU code, we conduct an empirical study on...
Accelerators such as graphics processing units (GPUs) provide an inexpensive way of improving the performance of cluster systems. In such an arrangement, the individual nodes of the cluster are directly connected to one or more accelerator devices via PCI Express. This results in a static mapping of accelerators onto compute nodes, where each accelerator can only be accessed from exactly one compute...
Graphics Processing Units (GPUs) are becoming the workhorse of scalable computations. MADNESS is a scientific framework used especially for computational chemistry. Most MADNESS applications use operators that involve many small tensor computations, resulting in a less regular organization of computations on GPUs. A single GPU kernel may have to multiply by hundreds of small square matrices (with...
Branch-and-Bound (B&B) algorithms are time-intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the use of GPU computing as a major complementary way to speed up those methods. The focus is put on the bounding mechanism of B&B algorithms, which is the most time consuming part of their exploration process...
Finite-difference, stencil-based discretization approaches are widely used in the solution of partial differential equations describing physical phenomena. Newton-Krylov iterative methods commonly used in stencil-based solutions generate matrices that exhibit diagonal sparsity patterns. To exploit these structures on modern GPUs, we extend the standard diagonal sparse matrix representation and define...
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