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2010 Proceeding- General Chairs:
- David Kaeli,
- Miriam Leeser
This is the third year for GPGPU. The pace of adoption of GPUs for both high-performance and general-purpose computing domains is accelerating. The class of applications being migrated to these devices is quickly expanding, and with the promise of new platforms on the horizon, we expect this growth to continue. The introduction of OpenCL is another catalyst that will accelerate the adoption of GPUs in the near future.
This year we received 30 high quality submissions. We are pleased to present these 12 high quality papers that were selected for the final program of GPGPU-3. The goal of this workshop is to provide a forum to discuss these general purpose programming environments and platforms, as well as describe successful applications that have leveraged this approach to acceleration. This year's workshop focuses on a range of new exciting applications, as well as new work in GPU languages, libraries, benchmarks, and models.
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Toward exascale computational science with heterogeneous processing
Computational requirements for scientific simulation continue to grow in scale and complexity. Meanwhile, HPC systems and centers are facing urgent constraints of power and thermal limits, while continuing to advance computational science. Our ...
Parallel multiclass classification using SVMs on GPUs
The scaling of serial algorithms cannot rely on the improvement of CPUs anymore. The performance of classical Support Vector Machine (SVM) implementations has reached its limit and the arrival of the multi core era requires these algorithms to adapt to ...
Cortical architectures on a GPGPU
As the number of devices available per chip continues to increase, the computational potential of future computer architectures grows likewise. While this is a clear benefit for future computing devices, future chips will also likely suffer from more ...
Compiling Python to a hybrid execution environment
A new compilation framework enables the execution of numerical-intensive applications, written in Python, on a hybrid execution environment formed by a CPU and a GPU. This compiler automatically computes the set of memory locations that need to be ...
Modeling GPU-CPU workloads and systems
Heterogeneous systems, systems with multiple processors tailored for specialized tasks, are challenging programming environments. While it may be possible for domain experts to optimize a high performance application for a very specific and well ...
Implementing the PGI Accelerator model
The PGI Accelerator model is a high-level programming model for accelerators, such as GPUs, similar in design and scope to the widely-used OpenMP directives. This paper presents some details of the design of the compiler that implements the model, ...
A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction
- Allen Leung,
- Nicolas Vasilache,
- Benoît Meister,
- Muthu Baskaran,
- David Wohlford,
- Cédric Bastoul,
- Richard Lethin
Programmers for GPGPU face rapidly changing substrate of programming abstractions, execution models, and hardware implementations. It has been established, through numerous demonstrations for particular conjunctions of application kernel, programming ...
GPGPU role within a 500 TFLOPS heterogeneous cluster
The outstanding price-performance of GPGPU technology has made it a key architectural engine within a 500 TFLOPS Heterogeneous Cluster being assembled by the Air Force Research Laboratory in Rome, NY. This new machine will likely be the largest ...
The Scalable Heterogeneous Computing (SHOC) benchmark suite
- Anthony Danalis,
- Gabriel Marin,
- Collin McCurdy,
- Jeremy S. Meredith,
- Philip C. Roth,
- Kyle Spafford,
- Vinod Tipparaju,
- Jeffrey S. Vetter
Scalable heterogeneous computing systems, which are composed of a mix of compute devices, such as commodity multicore processors, graphics processors, reconfigurable processors, and others, are gaining attention as one approach to continuing performance ...
Accelerating MATLAB Image Processing Toolbox functions on GPUs
In this paper, we present our effort in developing an open-source GPU (graphics processing units) code library for the MATLAB Image Processing Toolbox (IPT). We ported a dozen of representative functions from IPT and based on their inherent ...
Best-effort semantic document search on GPUs
Semantic indexing is a popular technique used to access and organize large amounts of unstructured text data. We describe an optimized implementation of semantic indexing and document search on manycore GPU platforms. We observed that a parallel ...
Accelerating SQL database operations on a GPU with CUDA
Prior work has shown dramatic acceleration for various database operations on GPUs, but only using primitives that are not part of conventional database languages such as SQL. This paper implements a subset of the SQLite command processor directly on ...
Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems
The Local Outlier Factor (LOF) is a very powerful anomaly detection method available in machine learning and classification. The algorithm defines the notion of local outlier in which the degree to which an object is outlying is dependent on the density ...
Iterative induced dipoles computation for molecular mechanics on GPUs
In this work, we present a first step towards the efficient implementation of polarizable molecular mechanics force fields with GPU acceleration. The computational bottleneck of such applications is found in the treatment of electrostatics, where higher-...
- Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
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Accelerating reaction–diffusion simulations with general-purpose graphics processing units
Summary We present a massively parallel stochastic simulation algorithm (SSA) for reaction-diffusion systems implemented on Graphics Processing Units (GPUs). These are designated chips optimized to process a high number of floating point operations ...