February 11th, 2015
February 10th, 2015
The cellular process responsible for providing energy for most life on Earth, namely, photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. We present, in an accompanying movie, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movie is the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. We describe the techniques that were used to build, simulate, analyze, and visualize the structures shown in the movie, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers.
Visualization of Energy Conversion Processes in a Light Harvesting Organelle at Atomic Detail. M. Sener, J. E. Stone, A. Barragan, A. Singharoy, I. Teo, K. L. Vandivort, B. Isralewitz, B. Liu, B. Goh, J. C. Phillips, L. F. Kourkoutis, C. N. Hunter, and K. Schulten. SC’14 Visualization and Data Analytics Showcase, 2014. Paper PDF
February 10th, 2015
High Performance Graphics is the leading international forum for performance-oriented graphics and imaging systems research, including innovative algorithms, efficient implementations, languages, parallelism, compilers, hardware and architectures for high-performance graphics. The conference brings together researchers, engineers, and architects to discuss the complex interactions of parallel hardware, novel programming models, and efficient algorithms in the design of systems for current and future graphics and visual computing applications.
High Performance Graphics is co-located with SIGGRAPH 2015 in Los Angeles, United States, and will take place on August 7–9, 2015.
February 10th, 2015
Initially introduced as special-purpose accelerators for graphics applications, graphics processing units (GPUs) have now emerged as general purpose computing platforms for a wide range of applications. To address the requirements of these applications, modern GPUs include sizable hardware-managed caches. However, several factors, such as unique architecture of GPU, rise of CPU-GPU heterogeneous computing, etc., demand effective management of caches to achieve high performance and energy efficiency. Recently, several techniques have been proposed for this purpose. In this paper, we survey several architectural and system-level techniques proposed for managing and leveraging GPU caches. We also discuss the importance and challenges of cache management in GPUs. The aim of this paper is to provide the readers insights into cache management techniques for GPUs and motivate them to propose even better techniques for leveraging the full potential of caches in the GPUs of tomorrow.
Sparsh Mittal, “A Survey Of Techniques for Managing and Leveraging Caches in GPUs”, Journal of Circuits, Systems, and Computers (JCSC), vol. 23, no. 8, 2014. WWW
December 27th, 2014
Recent years have witnessed a phenomenal growth in the computational capabilities and applications of GPUs. However, this trend has also led to dramatic increase in their power consumption. This paper surveys research works on analyzing and improving energy efficiency of GPUs. It also provides a classification of these techniques on the basis of their main research idea. Further, it attempts to synthesize research works which compare energy efficiency of GPUs with other computing systems, e.g. FPGAs and CPUs. The aim of this survey is to provide researchers with knowledge of state-of-the-art in GPU power management and motivate them to architect highly energy-efficient GPUs of tomorrow.
Sparsh Mittal, Jeffrey S Vetter, “A Survey of Methods for Analyzing and Improving GPU Energy Efficiency”, in ACM Computing Surveys, vol. 47, no. 2, pp. 19:1-19:23, 2014. [WWW]
December 2nd, 2014
Boost.Compute is an open-source, header-only C++ library for GPGPU and parallel-computing based on OpenCL. It provides a low-level C++ wrapper over OpenCL and high-level STL-like API with containers and algorithms for the GPU. Boost.Compute is available on GitHub and its documentation can be found here. See the full announcement here: http://kylelutz.blogspot.com/2014/12/boost-compute-0.4-released.html
November 14th, 2014
The wide majority of current state-of-the-art compressed GPU volume renderers are based on block-transform coding, which is susceptible to blocking artifacts, particularly at low bit-rates. In this paper the authors address the problem for the first time, by introducing a specialized deferred filtering architecture working on block-compressed data and including a novel deblocking algorithm. The architecture efficiently performs high quality shading of massive datasets by closely coordinating visibility- and resolution-aware adaptive data loading with GPU-accelerated per-frame data decompression, deblocking, and rendering. A thorough evaluation including quantitative and qualitative measures demonstrates the performance of our approach on large static and dynamic datasets including a massive 512^4 turbulence simulation (256GB), which is aggressively compressed to less than 2 GB, so as to fully upload it on graphics board and to explore it in real-time during animation.
(Fabio Marton, José Antonio Iglesias Guitián, Jose Díaz and Enrico Gobbetti: “Real-time deblocked GPU rendering of compressed volumes”. Proc. 19th International Workshop on Vision, Modeling and Visualization (VMV), pp. 167-174, Oct. 2014. [WWW])
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.
November 3rd, 2014
PARALUTION is a library for sparse iterative methods which can be performed on various parallel devices, including multi-core CPU, GPU (CUDA and OpenCL) and Intel Xeon Phi. The new 0.8.0 release provides the following extra features:
- Complex support
- TNS, Variable preconditioner
- BiCGStab(l), QMRCGStab, FCG solvers
- RS and PairWise AMG
- SIRA eigenvalue solver
- Replace/Extract column/row functions
- Stencil computation
For details, visit http://www.paralution.com.
November 3rd, 2014
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])
This webinar provides an overview of the improved analysis performance tools available in CUDA 6.0 and key optimization strategies for compute, latency and memory bound problems. The webinar includes techniques for ensuring peak utilization of CUDA cores, how to improve branching efficiency, intrinsic functions and loop unrolling. Optimal access patterns for global and shared memory are presented, including a comparison between the Fermi and Kepler architectures. To view the webinar go to: http://acceleware.com/blog/webinar-essential-cuda-optimization-techniques
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