Research of Acceleration MS-Alignment Identifying Post-Translational Modifications on GPU

The graphics processing unit (GPU) has evolved into a key part of today's heterogeneous parallel computing architecture. A number of influential data mining algorithms have been parallelized on GPUs including frequent pattern mining algorithms, such as Apriori. Unfortunately, due to two major challenges, the more effective method for mining frequent patterns without candidate generation named FP-Growth has not been implemented on GPUs. Firstly, it is very hard to efficiently build the FP-Tree in parallel on GPUs as it is an inherently sequential process. Secondly, mining the FP-Tree in parallel is also a difficult task. In this paper, we propose a fully parallel method to build the FP-Tree on CUDA-enabled GPUs and implement a novel parallel algorithm for mining all frequent patterns using the latest CUDA Dynamic Parallelism techniques. We show that, on a range of representative benchmark datasets, the proposed GPU-based FP-Growth algorithm can achieve significant speedups compared to the original algorithm.

In this paper, we explore the implementation of Monte Carlo based statistical static timing analysis (SSTA) on a Graphics Processing Unit (GPU). SSTA via Monte Carlo simulations is a computationally expensive, but important step required to achieve design timing closure. It provides an accurate estimate of delay variations and their impact on design yield. The large number of threads that can be computed in parallel on a GPU suggests a natural fit for the problem of Monte Carlo based SSTA to the GPU platform. Our implementation performs multiple delay simulations at a single gate in parallel. A parallel implementation of the Mersenne Twister pseudo-random number generator on the GPU, followed by Box-Muller transformations (also implemented on the GPU) is used for generating gate delay numbers from a normal distribution. The μ and σ of the pin-to-output delay distributions for all inputs and for every gate, are obtained using a memory lookup, which benefits from the large memory bandwidth of the GPU. Threads which execute in parallel have no data/control dependencies on each other. All threads compute identical instructions, but on different data, as required by the Single Instruction Multiple Data (SIMD) programming semantics of the GPU. Our approach is implemented on a NVIDIA GeForce GTX 8800 GPU card. Our results indicate that our approach can obtain an average speedup of about 260x as compared to a serial CPU implementation. With the recently announced quad 8800 GPU cards, we estimate that our approach would attain a speedup of over 785x. The correctness of the Monte Carlo based SSTA implemented on a GPU has been verified by comparing its results with a CPU based implementation.

This paper presents an implementation of a communication system which operates on a realtime basis by using a graphic processing unit (GPU). In order to cope with an extremely highspeed data rate in modern communication systems, system processor should support the high system throughput and high operation complexity. GPU can satisfy the requirement of high complexity by processing a lot of data in parallel. Also, GPU provides a convenient operation system which enables relatively easy implementation of various waveforms. In this paper, we implement WiBro system and demonstrate the performance in realtime.

Recently, IoT (Internet of Things) technologies have been progressed. To overcome of the high cost of developing IoT services by vertically integrating devices and services, Open IoT enables various IoT services to be developed by integrating horizontally separated devices and services. For Open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically and an automatic GPU (graphics processing unit) offloading technology as an elementary technology of Tacit Computing. However, it can improve limited applications because it only optimizes parallelizable loop statements extraction. Therefore, in this paper, to improve performances of more applications automatically, we propose an improved method with reduction of data transfer between CPU and GPU. This can improve performance of many IoT applications. We evaluate our proposed GPU offloading method by applying it to Darknet which is general large application for CPU and find that it can process it 3 times as quickly as only using CPUs within 10 h tuning time.

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 improvement from Relaxed Consistency Memory (RCM) models varies between 30-40 percent of total performance gain. The relaxed consistency memory model has been realized on a Graphics Processing Unit (GPU) using Open Computing Language (OpenCL) as the programming language. This memory model has been applied on a case study of high order matrix multiplication and their performance has been analysed in terms of two metrics: GPU Computation Percentage (GCP) and GPU Load Balance (GLB). With sufficient parallelism and high PCIe data transfer bandwidth, the RCM model on GPU gives the better performance in comparison to a sequential model on CPU.

This Human Skin Colour detection is analyzed to be a most suitable and strong cue for face detection and face recognition which finds various application in domain like surveillance, face biometrics, gesture recognition, interactive game application and many other. These applications require fast image processing in real time and demands enormously high performance with respect to time and processing speed. Earlier work done on sequential architecture does not provide required capability and can be achieved by parallel programming. As Image processing applications shows high degree of parallelism, they prove excellent source for multi-core platform. We can use GPU multithreaded parallel computing techniques to improve the speed of image processing. The paper proposes a data parallelism programming model for Human Skin Colour Detection algorithm. The objective is to increase the computational speed of the algorithm through data parallelism using CUDA framework. The framework is supported by OpenCV libraries and implemented by GPU (Graphics Processing Unit). The evaluation is done on basis of comparative analysis of serial and parallel programming computing. The pixel based Explicitly Defined Region skin classifier is used and RGB colour space is chosen due to its wider use in storing and processing of digital image data. To gain substantial acceleration in image computations, skin classifier code is off-loaded to GPU having compute capacity of 3.5 and rest of code is executed on CPU. We have tested module on static images, as well as on live camera captured images. SFA standard dataset is utilized to evaluate performance. Speedup of 23.45% was achieved using parallel programming.

Most tandem mass spectrometry (MS/MS) database search algorithms perform a restrictive search that takes into account only a few types of post-translational modifications (PTMs) and ignores all others. We describe an unrestrictive PTM search algorithm, MS-Alignment, that searches for all types of PTMs at once in a blind mode, that is, without knowing which PTMs exist in nature. Blind PTM identification makes it possible to study the extent and frequency of different types of PTMs, still an open problem in proteomics. Application of this approach to lens proteins resulted in the largest set of PTMs reported in human crystallins so far. Our analysis of various MS/MS data sets implies that the biological phenomenon of modification is much more widespread than previously thought. We also argue that MS-Alignment reveals some uncharacterized modifications that warrant further experimental validation.

Modern video compression algorithms put significant strain on a system's CPU, especially for video encoding. The ever increasing demands for using video compression algorithms in a wide range of applications necessitate the use of processing components that boost the speed and quality of the video compression algorithm's execution. The vast parallel computational capabilities of modern graphics processing units (GPUs) that usually remain underutilized makes them suitable for handling the processing load for video coding. This paper examines and evaluates the performance benefits of using the GPU over the CPU for an experimental video compression algorithm. An NVIDIA CUDA GPU implementation is evaluated against a traditional multithreaded CPU implementation. Experimental results show that at the highest resolution examined, the GPU approach achieved an impressive speedup ratio of 21.303x against the CPU for the decoding process, while for encoding, the speedup ratio reached 11.048x. Overall results indicate the prevalence of the GPU over the CPU, justified reasonably by the massive parallelism offered by the GPGPU computing paradigm, showing that the GPU should be the architecture of choice for high definition video coding.

Database searching is a main method for protein identification in shotgun proteomics, and many research efforts are dedicated to improving its effectiveness. However, the efficiency of database searching is facing a serious challenge, due to the ever fast growth of protein and peptide databases resulted from genome translations, enzymatic digestions, and post-translational modifications (PTMs). On the other hand, as a general-purpose and high performance parallel hardware, Graphics Processing Units (GPUs) develop continuously and provide another promising platform for parallelizing database searching based protein identification. It becomes very important to study how to speed up database search engines by GPUs for protein identification. In this paper, we mainly utilize GPUs to accelerate the scoring module, which is the most time-consuming component. Specifically, we study two popular scoring method: spectral dot product based method, which is used by X!Tandem, and kernel spectral dot product, which is used by pFind.

Recent advances in artificial intelligence has shown a direct correlation between the performance of a network and the number of hidden layers within the network. The Compute Unified Device Architecture (CUDA) framework facilitates the movement of heavy computation from the CPU to the graphics processing unit (GPU) and is used to accelerate the training of neural networks. In this paper, we consider the problem of data-parallel neural network training. We compare the performance of training the same neural network on the GPU with and without data parallelism. When data parallelism is used, we compare with both the conventional averaging of coefficients and our proposed method. We set out to show that not all sub-networks are equal and thus, should not be treated as equals when normalising weight vectors. The proposed method achieved state of the art accuracy faster than conventional training along with better classification performance in some cases.

In radiation transport calculations, the effects of material temperature on neutron/nucleus interactions must be taken into account through Doppler broadening adjustments to the microscopic cross section data. Historically, Monte Carlo transport simulations have accounted for this temperature dependence by interpolating among precalculated Doppler broadened cross sections at a variety of temperatures. More recently, there has been much interest in on-the-fly Doppler broadening methods, where reference data is broadened on-demand during particle transport to any temperature. Unfortunately, Doppler broadening operations are expensive on traditional central processing unit (CPU) architectures, making on-the-fly Doppler broadening unaffordable without approximations or complex data preprocessing. This work considers the use of graphics processing unit (GPU)s, which excel at parallel data processing, for on-the-fly Doppler broadening in continuous-energy Monte Carlo simulations. Two methods are considered for the broadening operations – a GPU implementation of the standard SIGMA1 algorithm and a novel vectorized algorithm that leverages the convolution properties of the broadening operation in an attempt to expose additional parallelism. Numerical results demonstrate that similar cross section lookup throughput is obtained for on-the-fly broadening on a GPU as cross section lookup throughput with precomputed data on a CPU, implying that offloading Doppler broadening operations to a GPU may enable on-the-fly temperature treatment of cross sections without a noticeable reduction in cross section processing performance in Monte Carlo transport codes.

We propose a generalized method for adapting and optimizing algorithms for efficient execution on modern graphics processing units (GPU). The method consists of several steps. First, build a control flow graph (CFG) of the algorithm. Next, transform the CFG into a tree of loops and merge non-parallelizable loops into parallelizable ones. Finally, map the resulting loops tree to the tree of GPU computational units, unrolling the algorithm’s loops as necessary for the match. The mapping should be performed bottom-up, from the lowest GPU architecture levels to the highest ones, to minimize off-chip memory access and maximize register file usage. The method provides programmer with a convenient and robust mental framework and strategy for GPU code optimization. We demonstrate the method by adapting to a GPU the DPLL backtracking search algorithm for solving the Boolean satisfiability problem (SAT). The resulting GPU version of DPLL outperforms the CPU version in raw tree search performance sixfold for regular Boolean satisfiability problems and twofold for irregular ones.

Performance Analysis and Optimization of Graphics Processing Unit

This study presents an asynchronous parallel strategy coordinating central processing unit (CPU) and graphic processing unit (GPU) to accelerate neighborhood operation (NO). Specifically, we propose a data partitioning method called multi-anchor task queuing and a task scheduling method called bi-direction task scheduling, which can support CPU and GPU to find the responsible data blocks rapidly and concurrently handle their tasks via a bi-direction merge. Moreover, we optimize the organization of threads distributed among the CPU and GPU. Experimental results show that when a 1.7 GB raster dataset is processed, the speedup ratio achieved by the proposed parallel algorithm reaches 29.63, which is 19% and 18% higher than those of the GPU and standard asynchronous parallel algorithm, respectively. Additionally, the load balance index is below 0.085, which is significantly better than the value achieved by a conventional algorithm. Thus, the strategy achieves a higher speedup ratio and more adaptable load balance, thereby accelerating the NO more efficiently. Further, the impacts of the data volume, computational intensity, organization mode of the GPU threads, and granularity of the GPU stream on the parallel efficiency are evaluated and discussed. We also test the efficiency of four other common NOs with our strategy.

Abstract. Thanks to the nature of the graphics processing, the newly released products offer highly parallel processing units with high-memory bandwidth and computational power of more than teraflops per second. The modern GPUs are not only powerful graphic engines but also they are high level parallel programmable processors with very fast computing capabilities and high-memory bandwidth speed compared to central processing units (CPU). Data-parallel computations can be shortly described as mapping data elements to parallel processing threads. The rapid development of GPUs programmability and capabilities attracted the attentions of researchers dealing with complex problems which need high level calculations. This interest has revealed the concepts of “General Purpose Computation on Graphics Processing Units (GPGPU)” and “stream processing”. The graphic processors are powerful hardware which is really cheap and affordable. So the graphic processors became an alternative to computer processors. The graphic chips which were standard application hardware have been transformed into modern, powerful and programmable processors to meet the overall needs. Especially in recent years, the phenomenon of the usage of graphics processing units in general purpose computation has led the researchers and developers to this point. The biggest problem is that the graphics processing units use different programming models unlike current programming methods. Therefore, an efficient GPU programming requires re-coding of the current program algorithm by considering the limitations and the structure of the graphics hardware. Currently, multi-core processors can not be programmed by using traditional programming methods. Event procedure programming method can not be used for programming the multi-core processors. GPUs are especially effective in finding solution for repetition of the computing steps for many data elements when high accuracy is needed. Thus, it provides the computing process more quickly and accurately. Compared to the GPUs, CPUs which perform just one computing in a time according to the flow control are slower in performance. This structure can be evaluated for various applications of computer technology. In this study covers how general purpose parallel programming and computational power of the GPUs can be used in photogrammetric applications especially direct georeferencing. The direct georeferencing algorithm is coded by using GPGPU method and CUDA (Compute Unified Device Architecture) programming language. Results provided by this method were compared with the traditional CPU programming. In the other application the projective rectification is coded by using GPGPU method and CUDA programming language. Sample images of various sizes, as compared to the results of the program were evaluated. GPGPU method can be used especially in repetition of same computations on highly dense data, thus finding the solution quickly.