May 27th, 2014
November 20th, 2013
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 0.7.0 version provides the following new features:
- Windows support – full windows support for all backends (CUDA, OpenCL, OpenMP)
- Assembling function – new OpenMP parallel assembling function for sparse matrices (includes an update function for time-dependent problems)
- Direct (dense) solvers (for very small problems)
- (Restricted) Additive Schwarz preconditioners
- MATLAB/Octave plug-in
To avoid OpenMP overhead for small sized problems, the library will compute in serial if the size of the matrix/vector is below a pre-defined threshold. Internally, the OpenCL backend has been modified for simplified cross platform compilation.
October 19th, 2013
VexCL is a modern C++ library created for ease of GPGPU development with C++. VexCL strives to reduce the amount of boilerplate code needed to develop GPGPU applications. The library provides a convenient and intuitive notation for vector arithmetic, reduction, sparse matrix-vector multiplication, etc. The source code is available under the permissive MIT license. As of v1.0.0, VexCL provides two backends: OpenCL and CUDA. Users may choose either of those at compile time with a preprocessor macro definition. More information is available at the GitHub project page and release notes page.
September 4th, 2013
PARALUTION is a library for sparse iterative methods which can be performed on various parallel devices, including multi-core CPU and GPU. In the new 0.4.0 version, the library provides also a backend for Xeon Phi (MIC). With this new version, various performance benchmarks based on vector-vector routines, sparse matrix-vector multiplication and CG method on different backends have been released: OpenMP/CUDA/OpenCL- NVIDIA GPU, AMD GPU, CPU and Xeon Phi. More information: http://www.paralution.com/benchmarks/
May 11th, 2013
Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many numerical algorithms and has been studied extensively on all modern processor and accelerator architectures. However, the optimal sparse matrix data storage format is highly hardware-specific, which could become an obstacle when using heterogeneous systems. Also, it is as yet unclear how the wide single instruction multiple data (SIMD) units in current multi- and many-core processors should be used most efficiently if there is no structure in the sparsity pattern of the matrix. We suggest SELL-C-sigma, a variant of Sliced ELLPACK, as a SIMD-friendly data format which combines long-standing ideas from General Purpose Graphics Processing Units (GPGPUs) and vector computer programming. We discuss the advantages of SELL-C-sigma compared to established formats like Compressed Row Storage (CRS) and ELLPACK, and show its suitability on a variety of hardware platforms (Intel Sandy Bridge, Intel Xeon Phi and Nvidia Tesla K20) for a wide range of test matrices from different application areas. Using appropriate performance models we develop deep insight into the data transfer properties of the SELL-C-sigma spMVM kernel. SELL-C-sigma comes with two tuning parameters whose performance impact across the range of test matrices is studied and for which reasonable choices are proposed. This leads to a hardware-independent (“catch-all”) sparse matrix format, which achieves very high efficiency for all test matrices across all hardware platforms.
(M. Kreutzer, G. Hager, G. Wellein, H. Fehske, and A. R. Bishop: “A unified sparse matrix data format for modern processors with wide SIMD units.” Submitted, July 2013 [preprint])
February 25th, 2013
Communicating data within the graphic processing unit (GPU) memory system and between the CPU and GPU are major bottlenecks in accelerating Krylov solvers on GPUs. Communication-avoiding techniques reduce the communication cost of Krylov subspace methods by computing several vectors of a Krylov subspace “at once,” using a kernel called “matrix powers.” The matrix powers kernel is implemented on a recent generation of NVIDIA GPUs and speedups of up to 5.7 times are reported for the communication-avoiding matrix powers kernel compared to the standards prase matrix vector multiplication (SpMV) implementation.
(M. Mehri Dehnavi, Y. El-Kurdi, J. Demmel and D. Giannacopoulos: “Communication-Avoiding Krylov Techniques on Graphic Processing Units”, IEEE Transactions on Magnetics 49(5):1749-1752, May 2013. [DOI])
February 7th, 2013
PARALUTION is a library for sparse iterative methods with special focus on multi-core and accelerator technology such as GPUs. In particular, it incorporates fine-grained parallel preconditioners designed to expolit modern multi-/many-core devices. Based on C++, it provides a generic and flexible design and interface which allow seamless integration with other scientific software packages. The library is open source and released under GPL. Key features are:
- OpenMP, CUDA and OpenCL support
- No special hardware/library requirement
- Portable code and results across all hardware
- Many sparse matrix formats
- Various iterative solvers/preconditioners
- Generic and robust design
- Plug-in for the finite element package Deal.II
- Documentation: user manual (pdf), reports, doxygen
More information, including documentation and case studies, is available at http://www.paralution.com.
December 21st, 2012
The paper presents techniques for generating very large finite-element matrices on a multicore workstation equipped with several graphics processing units (GPUs). To overcome the low memory size limitation of the GPUs, and at the same time to accelerate the generation process, we propose to generate the large sparse linear systems arising in finite-element analysis in an iterative manner on several GPUs and to use the graphics accelerators concurrently with CPUs performing collection and addition of the matrix fragments using a fast multithreaded procedure. The scheduling of the threads is organized in such a way that the CPU operations do not affect the performance of the process, and the GPUs are idle only when data are being transferred from GPU to CPU. This approach is verified on two workstations: the first consists of two 6-core Intel Xeon X5690 processors with two Fermi GPUs: each GPU is a GeForce GTX 590 with two graphics processors and 1.5 GB of fast RAM; the second workstation is equipped with two Tesla C2075 boards carrying 6 GB of RAM each and two 12-core Opteron 6174s. For the latter setup, we demonstrate the fast generation of sparse finite-element matrices as large as 10 million unknowns, with over 1 billion nonzero entries. Comparing with the single-threaded and multithreaded CPU implementations, the GPU-based version of the algorithm based on the ideas presented in this paper reduces the finite-element matrix-generation time in double precision by factors of 100 and 30, respectively.
(Dziekonski, A., Sypek, P., Lamecki, A. and Mrozowski, M.: “Generation of large finite-element matrices on multiple graphics processors”. International Journal on Numerical Methoths in Engineering, 2012, in press. [DOI])
December 3rd, 2012
amgcl is a simple and generic algebraic multigrid (AMG) hierarchy builder. Supported coarsening methods are classical Ruge-Stuben coarsening, and either plain or smoothed aggregation. The constructed hierarchy is stored and used with help of one of the supported backends including VexCL, ViennaCL, and CUSPARSE/Thrust.
With help of amgcl, solution of a large sparse system of linear equations may be easily accelerated through OpenCL, CUDA, or OpenMP technologies. Source code of the library is publicly available under MIT license at https://github.com/ddemidov/amgcl.
October 30th, 2012
The latest release 1.4.0 of the free open-source linear algebra library ViennaCL features the following highlights:
- Two computing backends in addition to OpenCL: CUDA and OpenMP
- Improved performance for (Block-) ILU0/ILUT preconditioners
- Optional level scheduling for ILU substitutions on GPUs
- Mixed-precision CG solver
- Initializer types from Boost.uBLAS (unit_vector, zero_vector, etc.)
Any contributions of fast CUDA or OpenCL computing kernels for future releases of ViennaCL are welcome! More information is available at http://viennacl.sourceforge.net.
We present new format for storing sparse matrices on GPU. We compare it with several other formats including CUSPARSE which is today probably the best choice for processing of sparse matrices on GPU in CUDA. Contrary to CUSPARSE which works with common CSR format, our new format requires conversion. However, multiplication of sparse-matrix and vector is significantly faster for many matrices. We demonstrate it on set of 1 600 matrices and we show for what types of matrices our format is profitable.
(Heller M., Oberhuber T.: “Improved Row-grouped CSR Format for Storing of Sparse Matrices on GPU”, Proceedings of Algoritmy 2012, 2012, Handlovičová A., Minarechová Z. and Ševčovič D. (ed.), pages 282-290, ISBN 978-80-227-3742-5) [ARXIV preprint]