February 11th, 2015
February 11th, 2015
Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper also reviews those techniques which use GPU and FPGA to improve energy efficiency of embedded systems. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow.
Sparsh Mittal, “A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems”, International Journal of Computer Aided Engineering and Technology (IJCAET), vol 6, no. 4, 2014. WWW
February 11th, 2015
GPUs play an increasingly important role in high-performance computing. While developing naive code is straightforward, optimizing massively parallel applications requires deep understanding of the underlying architecture. The developer must struggle with complex index calculations and manual memory transfers. This article classifies memory access patterns used in most parallel algorithms, based on Berkeley’s Parallel “Dwarfs.” It then proposes the MAPS framework, a device-level memory abstraction that facilitates memory access on GPUs, alleviating complex indexing using on-device containers and iterators. This article presents an implementation of MAPS and shows that its performance is comparable to carefully optimized implementations of real-world applications.
Rubin, Eri, et al. ["MAPS: Optimizing Massively Parallel Applications Using Device-Level Memory Abstraction."](http://dl.acm.org/citation.cfm?id=2680544) ACM Transactions on Architecture and Code Optimization (TACO) 11.4 (2014): 44.
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
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 2nd, 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]
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 3rd, 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.
October 29th, 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])
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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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 »
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