Abstract:

A Modern Graphics Processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two-dimensional Ising model in order to overcome the memory limitations of a single GPU which enables us to simulate significantly larger systems. Using multi-spin coding techniques, we are able to accelerate simulations on a single GPU by factors up to 35 compared to an optimized single Central Processor Unit (CPU) core implementation which employs multi-spin coding. By combining the Compute Unified Device Architecture (CUDA) with the Message Parsing Interface (MPI) on the CPU level, a single Ising lattice can be updated by a cluster of GPUs in parallel. For large systems, the computation time scales nearly linearly with the number of GPUs used. As proof of concept we reproduce the critical temperature of the 2D Ising model using finite size scaling techniques.

(Benjamin Block, Peter Virnau and Tobias Preis: “Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model”, Computer Physics Communications 181:9, 1549-1556, Sep. 2010. DOI Link. arXiv link)

Abstract:

This paper presents a complete modular approach to computing bivariate polynomial resultants on Graphics Processing Units (GPU). Given two polynomials, the algorithm first maps them to a prime field for sufficiently many primes, and then processes each modular image individually. We evaluate each polynomial at several points and compute a set of univariate resultants for each prime in parallel on the GPU. The remaining “combine” stage of the algorithm comprising polynomial interpolation and Chinese remaindering is also executed on the graphics processor. The GPU algorithm returns coefficients of the resultant as a set of Mixed Radix (MR) digits. Finally, the large integer coefficients are recovered from the MR representation on the host machine. With the approach of displacement structure and efficient modular arithmetic we have been able to achieve more than 100x speed-up over a CPU-based resultant algorithm from Maple 13.

(Pavel Emeliyanenko: “A complete modular resultant algorithm targeted for realization on graphics hardware”, Proceedings of the 4th International Workshop on Parallel and Symbolic Computation (PASCO2010), pages 35-43, Grenoble, France, July 2010. DOI link.  Direct PDF link.)

The Swarm-NG package helps scientists and engineers harness the power of GPUs. In the early releases, Swarm-NG will focus on the integration of an ensemble of N-body systems evolving under Newtonian gravity. Swarm-NG does not replicate existing libraries that calculate forces for large-N systems on GPUs, but rather focuses on integrating an ensemble of many systems where N is small. This is of particular interest for astronomers who study the chaotic evolution of planetary systems. In the long term, we hope Swarm-NG will allow for the efficient parallel integration of user-defined systems of ordinary differential equations.

Abstract:

We describe a parallel hybrid symplectic integrator for planetary system integration that runs on a graphics processing unit (GPU). The integrator identifies close approaches between particles and switches from symplectic to Hermite algorithms for particles that require higher resolution integrations. The integrator is approximately as accurate as other hybrid symplectic integrators but is GPU accelerated.

(Alexander Moore and Alice C. Quillen: “QYMSYM: A GPU-Accelerated Hybrid Symplectic Integrator That Permits Close Encounters”. preprint on arXiv, available code)

From the paper’s abstract:

A wide class of finite element electromagnetic applications requires computing very large sparse matrix vector multiplications (SMVM). Due to the sparsity pattern and size of the matrices, solvers can run relatively slowly. The rapid evolution of graphic processing units (GPUs) in performance, architecture and programmability make them very attractive platforms for accelerating computationally intensive kernels such as SMVM. This work presents a new algorithm to accelerate the performance of the SMVM kernel on graphic processing units.

From the paper’s conclusion:

We have introduced several efficient techniques to accelerate the execution of the sparse matrix vector multiplication (SMVM) on NVIDIA graphic processing units. The proposed methods increased the performance of the SMVM kernel on GT 8800 up to 18.8 times compared to the quad core CPU and 3 times compared to previous work by Bell and Garland on accelerating SMVM for GPUs.

(M. Mehri Dehnavi, D. Fernandez and D. Giannacopoulos: “Finite element sparse matrix vector multiplication on GPUs”. IEEE Transactions on Magnetics, vol. 46, no. 8, pp. 2982-2985, August 2010. DOI 10.1109/TMAG.2010.2043511)

Abstract:

Ocelot is a dynamic compilation framework designed to map the explicitly data parallel execution model used by NVIDIA CUDA applications onto diverse multithreaded platforms. Ocelot includes a dynamic binary translator from Parallel Thread eXecution ISA (PTX) to many-core processors that leverages the Low Level Virtual Machine (LLVM) code generator to target x86 and other ISAs. The dynamic compiler is able to execute existing CUDA binaries without recompilation from source and supports switching between execution on an NVIDIA GPU and a many-core CPU at runtime. It has been validated against over 130 applications taken from the CUDA SDK, the UIUC Parboil benchmark, the Virginia Rodinia benchmarks, the GPU-VSIPL signal and image processing library, the Thrust library, and several domain specific applications.

This paper presents a high level overview of the implementation of the Ocelot dynamic compiler highlighting design decisions and trade-offs, and showcasing their effect on application performance. Several novel code transformations are explored that are applicable only when compiling explicitly parallel applications and traditional dynamic compiler optimizations are revisited for this new class of applications. This study is expected to inform the design of compilation tools for explicitly parallel programming models (such as OpenCL) as well as future CPU and GPU architectures.

This paper identifies several key areas of research and open problems for optimizing the performance of data parallel programs (such as CUDA and OpenCL) that were encountered when designing a binary translator from PTX to LLVM/x86. The complete implementation of Ocelot is available open-source under the new BSD license at http://code.google.com/p/gpuocelot. Ongoing work involves translating PTX to AMD’s IL allowing CUDA programs to be executed on AMD GPUs, developing parallel-aware PTX to PTX optimizations, and exploring new programming and execution models that are layered on PTX.

(Gregory Diamos, Andrew Kerr, Sudhakar Yalamanchili and Nathan Clark: “Ocelot: A dynamic compiler for bulk-synchroneous applications in heterogeneous systems”. 19 International Conference on Parallel Architectures and Compilation Techniques (PACT2010), September 2010).

Simbios, the NIH Center for Biomedical Computation at Stanford University, is excited to announce the release of OPENMM 2.0.

OPENMM was designed to enhance the performance of almost any molecular dynamics simulation package (MD package) by allowing the code to be executed on high performance computer architectures, in particular Graphics Processing Units (GPUs). Most molecular dynamics packages can be modified to call OPENMM, resulting in significant acceleration on such high performance architectures, without changing the way users interact with the MD package. Read the rest of this entry »

A 30,000-hexahedron FEM model.

Abstract:

In this paper we present a GPU-based multigrid approach for simulating elastic deformable objects in real time. Our method is based on a finite element discretization of the deformable object using hexahedra. It draws upon recent work on multigrid schemes for the efficient numerical solution of partial differential equations on such discretizations. Due to the regular shape of the numerical stencil induced by the hexahedral regime, and since we use matrix-free formulations of all multigrid steps, computations and data layout can be restructured to avoid execution divergence and to support memory access patterns which enable the hardware to coalesce multiple memory accesses into single memory transactions. This enables to effectively exploit the GPU’s parallel processing units and high memory bandwidth via the CUDA parallel programming API. We demonstrate performance gains of up to a factor of 12 compared to a highly optimized CPU implementation. By using our approach, physics-based simulation at an object resolution of 64^3 is achieved at interactive rates.

(Christian Dick, Joachim Georgii and Rüdiger Westermann: “A Real-Time Multigrid Finite Hexahedra Method for Elasticity”, http://wwwcg.in.tum.de/Research/Publications/CompMechanics)

Abstract:

Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today’s multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an NVIDIA GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.

(Victor W. Lee, Changkyu Kim, Jatin Chhugani, Michael Deisher, Daehyun Kim, Anthony D. Nguyen, NadathurSatish, Mikhail Smelyanski, Srinivas Chennupaty, Per Hammarlund, Ronak Singhal and Pradeep Dubey: “Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU”, SIGARCH Computer Architecture News 38(3), pp. 451-460, June 2010. DOI Link.)

Abstract:

In the ongoing arms race against malware, antivirus soft-ware is at the forefront, as one of the most important defense tools in our arsenal. Antivirus software is flexible enough to be deployed from regular users desktops, to corporate e-mail proxies and file servers. Unfortunately, the signatures necessary to detect incoming malware number in the tens of thousands. To make matters worse, antivirus signatures area lot longer than signatures in network intrusion detection systems. This leads to extremely high computation costs necessary to perform matching of suspicious data against those signatures.In this paper, we present GrAVity, a massively parallel antivirus engine.Our engine utilized the compute power of modern graphics processors,that contain hundreds of hardware microprocessors. We have modified ClamAV, the most popular open source antivirus software, to utilize our engine. Our prototype implementation has achieved end-to-end throughput in the order of 20 Gbits/s, 100 times the performance of the CPU-only ClamAV, while almost completely offloading the CPU, leaving it free to complete other tasks. Our micro-benchmarks have measured our engine to be able to sustain throughput in the order of 40 Gbits/s. The results suggest that modern graphics cards can be used effectively to perform heavy-duty anti-malware operations at speeds that cannot be matched by traditional CPU based techniques.

(Giorgos Vasiliadis and Sotiris Ioannidis. “GrAVity: A Massively Parallel Antivirus Engine”. In Proceedings of the 13th International Symposium On Recent Advances In Intrusion Detection (RAID). September 2010, Ottawa, Canada. Link to PDF.)