CfP: 23rd High Performance Computing Symposium (HPC’15)

November 14th, 2014

The 23rd High Performance Computing Symposium (HPC’15) is held in conjunction with the SCS Spring Simulation Multiconference (SpringSim’15), April 12-15, 2015, in Alexandria, VA, USA.

Topics of interest include:

  • High performance/large scale application case studies
  • GPU for general purpose computations (GPGPU)
  • Multicore and many-core computing
  • Power aware computing
  • Cloud, distributed, and grid computing
  • Asynchronous numerical methods and programming
  • Hybrid system modeling and simulation
  • Large scale visualization and data management
  • Tools and environments for coupling parallel codes
  • Parallel algorithms and architectures
  • High performance software tools
  • Resilience at the simulation level
  • Component technologies for high performance computing

More information: http://hosting.cs.vt.edu/hpc2015.

Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization

November 3rd, 2014

Abstract:

The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations.

(Cazzaniga P., Nobile M.S., Besozzi D., Bellini M., Mauri G.: “Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization”. BioMed Research International, vol. 2014. [DOI])

CfP: Optimization of Parallel Scientific Applications with Accelerated HPC

October 29th, 2014

Since 2011, the most powerful supercomputers systems ranked in the Top500 list have been hybrid systems composed of thousands of nodes that includes CPUs and accelerators, as Xeon Phi and GPUs. Programming and deploying applications on those systems is still a challenge due to complexity of the system and the need to mix several programming interfaces (MPI, CUDA, Intel Xeon Phi) in the same application. This special issue of the International Journal of Computers & Electrical Engineering is aimed at exploring the state of the art of developing applications in accelerated massive HPC architectures, including practical issues of hybrid usage models with MPI, OpenMP, and other accelerators programming models. The idea is to publish novel work on the use of available programming interfaces (MPI, CUDA, Intel Xeon Phi) and tools for code development, application performance optimizations, application deployment on accelerated systems, as well as the advantages and limitations of accelerated HPC systems. Experiences with real-world applications, including scientific computing, numerical simulations, healthcare, energy, data-analysis, etc. are also encouraged.

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CfP: GPGPU 2015

October 29th, 2014

The goal of this workshop is to provide a forum to discuss new and emerging general-purpose purpose programming environments and platforms, as well as evaluate applications that have been able to harness the horsepower provided by these platforms. This year’s work is particularly interested on new heterogeneous GPU platforms, new forms of concurrency, and novel/irregular applications that can leverage these platforms. Papers are being sought on many aspects of GPUs, including (but not limited to): Read the rest of this entry »

Approximate TF–IDF based on topic extraction from massive message stream using the GPU

October 16th, 2014

Abstract:

The Web is a constantly expanding global information space that includes disparate types of data and resources. Recent trends demonstrate the urgent need to manage the large amounts of data stream, especially in specific domains of application such as critical infrastructure systems, sensor networks, log file analysis, search engines and more recently, social networks. All of these applications involve large-scale data-intensive tasks, often subject to time constraints and space complexity. Algorithms, data management and data retrieval techniques must be able to process data stream, i.e., process data as it becomes available and provide an accurate response, based solely on the data stream that has already been provided. Data retrieval techniques often require traditional data storage and processing approach, i.e., all data must be available in the storage space in order to be processed. For instance, a widely used relevance measure is Term Frequency–Inverse Document Frequency (TF–IDF), which can evaluate how important a word is in a collection of documents and requires to a priori know the whole dataset.
To address this problem, we propose an approximate version of the TF–IDF measure suitable to work on continuous data stream (such as the exchange of messages, tweets and sensor-based log files). The algorithm for the calculation of this measure makes two assumptions: a fast response is required, and memory is both limited and infinitely smaller than the size of the data stream. In addition, to face the great computational power required to process massive data stream, we present also a parallel implementation of the approximate TF–IDF calculation using Graphical Processing Units (GPUs).
This implementation of the algorithm was tested on generated and real data stream and was able to capture the most frequent terms. Our results demonstrate that the approximate version of the TF–IDF measure performs at a level that is comparable to the solution of the precise TF–IDF measure.

(Ugo Erra, Sabrina Senatore, Fernando Minnella and Giuseppe Caggianese: “Approximate TF-IDF based on topic extraction from massive message stream using the GPU”, Information Sciences 292, pp.141-163, Feb. 2015. [DOI])

CfP: 23rd High Performance Computing Symposium

September 5th, 2014

The 23rd High Performance Computing Symposium (April 12-15, 2015 in Alexandria, VA, USA) is devoted to the impact of high performance computing and communications on computer simulations. Topics of interest include:

  • GPU for general purpose computations (GPGPU)
  • Hybrid system modeling and simulation
  • Tools and environments for coupling parallel codes
  • Parallel algorithms and architectures
  • High performance software tools

Submission deadline for full papers: November 22, 2014. More information can be found at http://hosting.cs.vt.edu/hpc2015.

Accelerated Combinatorial Optimization using Graphics Processing Units and C++ AMP

August 20th, 2014

Abstract:

In the course of less than a decade, Graphics Processing Units (GPUs) have evolved from narrowly scoped application specific accelerators to general-purpose parallel machines capable of accommodating an ever-growing set of algorithms. At the same time, programming GPUs appears to have become trapped around an attractor characterised by ad-hoc practices, non-portable implementations and inexact, uninformative performance reporting. The purpose of this paper is two-fold, on one hand pursuing an in-depth look at GPU hardware and its characteristics, and on the other demonstrating that portable, generic, mathematically grounded programming of these machines is possible and desirable. An agent-based meta-heuristic, the Max-Min Ant System (MMAS), provides the context. The major contributions brought about by this article are the following: (1) an optimal, portable, generic-algorithm based MMAS implementation is derived; (2) an in-depth analysis of AMD’s Graphics Core Next (GCN) GPU and the C++ AMP programming model is supplied; (3) a more robust approach to performance reporting is presented; (4) novel techniques for raising the abstraction level without sacrificing performance are employed. This represents the first implementation of an algorithm from the Ant Colony Optimisation (ACO) family using C++ AMP, whilst at the same time being one of the first uses of the latter programming environment.

(A. Voicu: “Accelerated Combinatorial Optimization using Graphics Processing Units and C++ AMP ”. International Journal of Computer Applications 100(6):21-30, August 2014. [DOI])

Call for Contributions: GPU algorithms for image processing and computer vision

July 22nd, 2014

“GPU Algorithms for Image Processing and Computer Vision”, to be published by Springer, will contain a collection of articles on fundamental image processing and computer vision methods adapted for Graphics Processing Units (GPUs). In recent years, substantial efforts were undertaken to adapt many such algorithms for massively-parallel GPU-based systems. The book is envisioned as a consolidation of such work into a single volume covering widely used methods and techniques. Each chapter will be written by authors working on a specific group of methods. It will provide mathematical background, parallel algorithm, and implementation details leading to reusable, adaptable, and scalable code fragments. The book will serve as a GPU implementation manual for many image processing and analysis algorithms providing valuable insights into parallelization strategies for GPUs as well as ready-to-use code fragments with a broad appeal to both developers and researchers interested in GPU computing. Read the rest of this entry »

On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications

June 8th, 2014

Abstract:

Many current high-performance clusters include one or more GPUs per node in order to dramatically reduce application execution time, but the utilization of these accelerators is usually far below 100%. In this context, emote GPU virtualization can help to reduce acquisition costs as well as the overall energy consumption. In this paper, we investigate the potential overhead and bottlenecks of several “heterogeneous” scenarios consisting of client GPU-less nodes running CUDA applications and remote GPU-equipped server nodes providing access to NVIDIA hardware accelerators. The experimental evaluation is performed using three general-purpose multicore processors (Intel Xeon, Intel Atom and ARM Cortex A9), two graphics accelerators (NVIDIA GeForce GTX480 and NVIDIA Quadro M1000), and two relevant scientific applications (CUDASW++ and LAMMPS) arising in bioinformatics and molecular dynamics simulations.

(A. Castelló, J. Duato, R. Mayo, A. J. Peña, E. S. Quintana-Ortí, V. Roca, and F. Silla, “On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications”. Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies, ENERGY 2014, Chamonix (France), pp. 57–62, 20 – 24 April 2014. [PDF])

Improving Cache Locality for GPU-based Volume Rendering

June 8th, 2014

Abstract:

We present a cache-aware method for accelerating texture-based volume rendering on a graphics processing unit (GPU). Because a GPU has hierarchical architecture in terms of processing and memory units, cache optimization is important to maximize performance for memory-intensive applications. Our method localizes texture memory reference according to the location of the viewpoint and dynamically selects the width and height of thread blocks (TBs) so that each warp, which is a series of 32 threads processed simultaneously, can minimize memory access strides. We also incorporate transposed indexing of threads to perform TB-level cache optimization for specific viewpoints. Furthermore, we maximize TB size to exploit spatial locality with fewer resident TBs. For viewpoints with relatively large strides, we synchronize threads of the same TB at regular intervals to realize synchronous ray propagation. Experimental results indicate that our cache-aware method doubles the worst rendering performance compared to those provided by the CUDA and OpenCL software development kits.

(Yuki Sugimoto, Fumihiko Ino, and Kenichi Hagihara: “Improving Cache Locality for GPU-based Volume Rendering”. Parallel Computing 40(5/6): 59-69, May 2014. [DOI])

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