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.
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
Developed in partnership with NVIDIA, this hands-on four day course will teach you how to write and optimize applications that fully leverage the multi-core processing capabilities of the GPU. This course will have a finance focus. Commonly used algorithms such as random number generation and Monte Carlo simulations will be used and profiled in examples. A background in finance is not necessary. For more information please visit: http://acceleware.com/training/988
CUDPP release 2.2 is a feature release that adds a new parallel primitive and improves some existing primitives. We have added cudppSuffixArray, a parallel skew algorithm (SA) implementation that computes the suffix array of a string. This suffix array primitive is now used in burrowsWheelerTransform, delivering better performance than CUDPP 2.1’s use of cudppStringSort. The new BWT is further used in cudppCompress, which is now faster than the original parallel compression and supports compression of text containing all possible unsigned char values. Some bugs in cudppMoveToFrontTransform and cudppStringSort have also been fixed. OS X users might also be interested in how we supported the use of OS X’s clang compiler in OS X Mavericks (10.9).
This hands-on four day course teaches how to write and optimize applications that fully leverage the multi-core processing capabilities of the GPU. More details and registration: http://acceleware.com/training/986
Hybrid Fortran is an Open Source directive based extension for the Fortran language. It is a way for HPC programmers to keep writing Fortran code like they are used to – only now with GPGPU support. It achieves performance portability by allowing different storage orders and loop structures for the CPU and GPU version. All computational code stays the same as in the respective CPU version, e.g. it can be kept in a low dimensionality even when the GPU version needs to be privatised in more dimensions in order to achieve a speedup. Hybrid Fortran takes care of the necessary transformations at compile-time (so there is no runtime overhead). A (python based) preprocessor parses these annotations together with the Fortran user code structure, declarations, accessors and procedure calls, and then writes separate versions of the code – once for CPU with OpenMP parallelization and once for GPU with CUDA Fortran. More details: http://typhooncomputing.com/?p=416
The course on Antenna Synthesis (with elements of GPU computing) is organized in the framework of the European School of Antennas. The course will take place at the Partenope Conference Center of the Università di Napoli Federico II, Napoli, Italy, on October 13-17, 2014. It faces three topics corresponding to the two main aspects of Antenna Synthesis, namely external and internal synthesis, and to numerical and implementation issues on High Performance Computing (HPC) platforms of synthesis algorithms. For details about the course please see this brochure and http://www.antennasvce.org/Community/Education/Courses?id_folder=533.
This blog entry provides an introduction to GPU virtualization, reviewing the five major technology vendors and their virtualization support for CUDA.
A new book titled “Numerical Computations with GPUs” has been published:
This book brings together research on numerical methods adapted for Graphics Processing Units (GPUs). It explains recent efforts to adapt classic numerical methods, including solution of linear equations and FFT, for massively parallel GPU architectures. This volume consolidates recent research and adaptations, covering widely used methods that are at the core of many scientific and engineering computations. Each chapter is written by authors working on a specific group of methods; these leading experts provide mathematical background, parallel algorithms and implementation details leading to reusable, adaptable and scalable code fragments. This book also serves as a GPU implementation manual for many numerical algorithms, sharing tips on GPUs that can increase application efficiency. The valuable insights into parallelization strategies for GPUs are supplemented by ready-to-use code fragments. Numerical Computations with GPUs targets professionals and researchers working in high performance computing and GPU programming. Advanced-level students focused on computer science and mathematics will also find this book useful as secondary text book or reference.
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