Usage Of Graphics Processing Units Research Articles

A vendor-agnostic, single code-based GPU tracking for the Inner Tracking System of the ALICE experiment

During the LHC Run 3 the ALICE online computing farm will process up to 50 times more Pb-Pb events per second than in Run 2. The implied computing resource scaling requires a shift in the approach that comprises the extensive usage of Graphics Processing Units (GPU) for the processing. We will give an overview of the state of the art for the data reconstruction on GPUs in ALICE, with additional focus on the Inner Tracking System detector. A detailed teardown of adopted techniques, implemented algorithms and approaches and performance report will be shown. Additionally, we will show how we support different GPUs brands (NVIDIA and AMD) with a single code-base using an automatic code translation and generation for different target architectures. Strengths and possible weaknesses of this approach will be discussed. Finally, an overview of the next steps towards an even more comprehensive usage of GPUs in ALICE software will be illustrated.

Extending the usage of graphics processing units on the cloud for cost savings on seismic data regularization

ABSTRACT. The usage of graphics processing units is already known as an alternative to traditional multi-core CPU processing, offering faster performance in the order of dozens of times in parallel tasks. Another new computing paradigm is cloud computing usage as a replacement to traditional in-house clusters, enabling seemingly unlimited computation power, no maintenance costs, and cutting-edge technology, dynamically on user demand. Previously those two tools were used to accelerate the estimation of Common Reflection Surface (CRS) traveltime parameters, both in zero-offset and finite-offset domain, delivering very satisfactory results with large time savings from GPU devices alongside cost savings on the cloud. This work extends those results by using GPUs on the cloud to accelerate the Offset Continuation Trajectory (OCT) traveltime parameter estimation. The results have shown that the time and cost savings from GPU devices’ usage are even larger than those seen in the CRS results, being up to fifty times faster and sixty times cheaper. This analysis reaffirms that it is possible to save both time and money when using GPU devices on the cloud and concludes that the larger the data sets are and the more computationally intensive the traveltime operators are, we can see larger improvements.Keywords: cloud computing, GPU, seismic processing. Estendendo o uso de placas gráficas na nuvem para economias em regularização de dados sísmicosRESUMO. O uso de aceleradores gráficos para processamento já é uma alternativa conhecida ao uso de CPUs multi-cores, oferecendo um desempenho na ordem de dezenas de vezes mais rápido em tarefas paralelas. Outro novo paradigma de computação é o uso da nuvem computacional como substituta para os tradicionais clusters internos, possibilitando o uso de um poder computacional aparentemente infinito sem custo de manutenção e com tecnologia de ponta, dinamicamente sob demanda de usuário. Anteriormente essas duas ferramentas foram utilizadas para acelerar a estimação de parâmetros do tempo de trânsito de Common Reflection Surface (CRS), tanto em zero-offset quanto em offsets finitos, obtendo resultados satisfatórios com amplas economias tanto de tempo quanto de dinheiro na nuvem. Este trabalho estende os resultados obtidos anteriormente, desta vez utilizando GPUs na nuvem para acelerar a estimação de parâmetros do tempo de trânsito em Offset Continuation Trajectory (OCT). Os resultados obtidos mostraram que as economias de tempo e dinheiro foram ainda maiores do que aquelas obtidas no CRS, sendo até cinquenta vezes mais rápido e sessenta vezes mais barato. Esta análise reafirma que é possível economizar tanto tempo quanto dinheiro usando GPUs na nuvem, e conclui que quanto maior for o dado e quanto mais computacionalmente intenso for o operador, maiores serão os ganhos de desempenho observados e economias.Palavras-chave: computação em nuvem, GPU, processamento sísmico.

New method to search for continuous gravitational waves from unknown neutron stars in binary systems

Non-axysymmetric, fast-spinning unknown neutron stars in binary systems may emit continuous gravitational waves (nearly-monochromatic long-duration signals) which can be detected by ground-based detectors like LIGO and Virgo. In this paper we present a new pipeline, called $\textit{BinarySkyHough}$, that can carry out all-sky searches for neutron stars in binary systems by exploiting the usage of graphics processing units. We give a detailed explanation of this new pipeline, and we present simulations which allow us to estimate the sensitivity of the new pipeline, which is approximately twice as sensitive as the best active pipeline with a comparable computational cost.

A survey of architectural approaches for improving GPGPU performance, programmability and heterogeneity

With the skyrocketing advances of process technology, the increased need to process huge amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing Units (GPUs) for General Purpose Computing becomes a trend and natural. GPUs have high computational power and excellent performance per watt, for data parallel applications, relative to traditional multicore processors. GPUs appear as discrete or embedded with Central Processing Units (CPUs), leading to a scheme of heterogeneous computing. Heterogeneous computing brings as many challenges as it brings opportunities. To get the most of such systems, we need to guarantee high GPU utilization, deal with irregular control flow of some workloads, and struggle with far-friendly-programming models. The aim of this paper is to provide a survey about GPUs from two perspectives: (1) architectural advances to improve performance and programmability and (2) advances to enhance CPU–GPU integration in heterogeneous systems. This will help researchers see the opportunities and challenges of using GPUs for general purpose computing, especially in the era of big data and the continuous need of high-performance computing.

Параллельная реализация алгоритма поиска минимальных остовных деревьев с использованием центрального и графического процессоров

Solution of the finding a minimum spanning tree problem is common in various areas of research: recognition of different objects, computer vision, analysis and construction of networks (eg, telephone, electrical, computer, travel, etc.), chemistry and biology, and many others. Processing large graphs is a quite time-consuming task for the central processor (CPU), and in high demand at the present moment. The usage of Graphics processing units (GPUs) as a mean to solve general-purpose problems grows every day, because GPUs have more computing power than CPUs. This article describes the methods of compression and conversion of graphs in standard formats to increase the efficiency of their processing. The search algorithm of minimum spanning trees has been used for researching the proposed approaches. The possibility of a hybrid implementation of this algorithm has been investigated. The highest results were obtained on the large R-MAT and SSCA2 graphs.

Graphics Processors in HEP Low-Level Trigger Systems

Usage of Graphics Processing Units (GPUs) in the so called general-purpose computing is emerging as an effective approach in several fields of science, although so far applications have been employing GPUs typically for offline computations. Taking into account the steady performance increase of GPU architectures in terms of computing power and I/O capacity, the real-time applications of these devices can thrive in high-energy physics data acquisition and trigger systems. We will examine the use of online parallel computing on GPUs for the synchronous low-level trigger, focusing on tests performed on the trigger system of the CERN NA62 experiment. To successfully integrate GPUs in such an online environment, latencies of all components need analysing, networking being the most critical. To keep it under control, we envisioned NaNet, an FPGA-based PCIe Network Interface Card (NIC) enabling GPUDirect connection. Furthermore, it is assessed how specific trigger algorithms can be parallelized and thus benefit from a GPU implementation, in terms of increased execution speed. Such improvements are particularly relevant for the foreseen Large Hadron Collider (LHC) luminosity upgrade where highly selective algorithms will be essential to maintain sustainable trigger rates with very high pileup.

Accelerating turbo similarity searching in chemoinformatics on multicore and GPU platforms

The increase in the database size of chemical compound requires a longer time in processing for any searching algorithms [1]. With the focus on an algorithm called Turbo Similarity Searching which have been proven to have good accuracy in retrieving actives [2], we propose that this algorithm be implemented on the widely-used many-cores and multi-cores processors which are easily available at lower cost. This would help medicinal chemist runs virtual screening faster while maintaining the accuracy. The many-cores processors are on-chip processors that could run simultaneously at a clock-cycle. Whilst the multi-cores processors are originally being developed to support graphics processing hence being called Graphics Processing Unit (GPU). However, the usage of GPU is now being generalized to include other general purpose operation [3]. Many works on the parallel field have tried implementing computational algorithms on this unit [4]. Taken into consideration on the compute intensive characteristics of TSS, we investigate the best method to parallelize it for better execution time. TSS is a two-phase algorithm in which its second phase is a compute intensive portion of the algorithm. Hence, if this phase could be accelerated, TSS would run faster and searching through a larger database would not be less a problem. This poster describes our experimental details and results on the matter.

MPI-CUDA sparse matrix–vector multiplication for the conjugate gradient method with an approximate inverse preconditioner

The preconditioned conjugate gradient (PCG) is one of the most prominent iterative methods for the solution of sparse linear systems with symmetric and positive definite matrix that arise, for example, in the modeling of incompressible flows. The method relies on a set of basic linear algebra operations which determine the overall performance. Therefore, to achieve improvements in the performance, implementations of these basic operations must be adapted to the changes in the architecture of parallel computing systems. In the last years, one of the strategies to increase the computing power of supercomputers has been the usage of Graphics Processing Units (GPUs) as math co-processors in addition to CPUs. This paper presents a MPI-CUDA implementation of the PCG solver for such hybrid computing systems composed of multiple CPUs and GPUs. Special attention has been paid to the sparse matrix–vector multiplication (SpMV), because most of the execution time of the solver is spent on this operation. The approximate inverse preconditioner, which is used to improve the convergence of the CG solver, is also based on the SpMV operation. An overlapping of data transfer and computations is proposed in order to hide the MPI and the CPU-GPU communications needed to perform parallel SpMVs. This strategy has shown a considerable improvement and, as a result, the hybrid implementation of the PCG solver has demonstrated a significant speedup compared to the CPU-only implementation.

ORTHORECTIFICATION BY USING GPGPU METHOD

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.

High-Performance Solution of Hierarchical Equations of Motion for Studying Energy Transfer in Light-Harvesting Complexes.

Excitonic models of light-harvesting complexes, where the vibrational degrees of freedom are treated as a bath, are commonly used to describe the motion of the electronic excitation through a molecule. Recent experiments point toward the possibility of memory effects in this process and require one to consider time nonlocal propagation techniques. The hierarchical equations of motion (HEOM) were proposed by Ishizaki and Fleming to describe the site-dependent reorganization dynamics of protein environments ( J. Chem. Phys. 2009 , 130 , 234111 ), which plays a significant role in photosynthetic electronic energy transfer. HEOM are often used as a reference for other approximate methods but have been implemented only for small systems due to their adverse computational scaling with the system size. Here, we show that HEOM are also solvable for larger systems, since the underlying algorithm is ideally suited for the usage of graphics processing units (GPU). The tremendous reduction in computational time due to the GPU allows us to perform a systematic study of the energy-transfer efficiency in the Fenna-Matthews-Olson (FMO) light-harvesting complex at physiological temperature under full consideration of memory effects. We find that approximative methods differ qualitatively and quantitatively from the HEOM results and discuss the importance of finite temperature to achieving high energy-transfer efficiencies.

Usage of graphics processing units for solving molecular dynamics problems

The paper presents results on performance and efficiency of GPU utilization in a simulation of molecular dynamics processes. The simulation was done with the usage of Lennard-Jones potential and leapfrog computational scheme.

Air pollution modelling using a Graphics Processing Unit with CUDA

The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) – a parallel computing architecture – has been developed by NVIDIA to utilize this performance in general purpose computations. Here we show for the first time a possible application of GPU for environmental studies serving as a basement for decision making strategies. A stochastic Lagrangian particle model has been developed on CUDA to estimate the transport and the transformation of the radionuclides from a single point source during an accidental release. Our results show that parallel implementation achieves typical acceleration values in the order of 80–120 times compared to CPU using a single-threaded implementation on a 2.33 GHz desktop computer. Only very small differences have been found between the results obtained from GPU and CPU simulations, which are comparable with the effect of stochastic transport phenomena in atmosphere. The relatively high speedup with no additional costs to maintain this parallel architecture could result in a wide usage of GPU for diversified environmental applications in the near future.