September 22nd, 2012
August 10th, 2012
The “Ludwig” lattice Boltzmann fluid dynamics application is a versatile application capable of simulating the hydrodynamics of complex fluids, (e.g. mixtures, surficants, liquid crystals, particle suspensions) to allow cutting-edge research into condensed matter physics. On October 3, Dr. Alan Gray from the University of Edinburgh presents a webinar on his team’s experiences in scaling the application on the Cray XK6 hybrid supercomputer. The presentation will cover:
- A review of excellent scaling up to O(1000) GPUs
- Steps taken to maximize performance on each GPU
- Designing the communication to allow efficient usage of many GPUs in parallel, including the overlapping of several stages using CUDA stream functionality
- Advanced functionality, including how to include colloidal particles in the simulation while minimizing data transfer overheads
Register at http://www.gputechconf.com/page/gtc-express-webinar.html.
June 27th, 2012
The MOSIX group announces the release of the Virtual OpenCL (VCL) cluster platform version 1.14. This version includes the SuperCL extension that allows micro OpenCL programs to run efficiently on devices of remote nodes. VCL provides an OpenCL platform in which all the cluster devices are seen as if they are located in the hosting-node. This platform benefits OpenCL applications that can use many devices concurrently. Applications written for VCL benefit from the reduced programming complexity of a single computer, the availability of shared-memory, multi-threads and lower granularity parallelism, as well as concurrent access to devices in many nodes. With SuperCL, a programmable sequence of kernels and/or memory operations can be sent to remote devices in cluster nodes, usually with just a single network round-trip. SuperCL also offers asynchronous communication with the host, to avoid the round-trip waiting time, as well as direct access to distributed file-systems. The VCL package can be downloaded from mosix.org.
December 14th, 2011
SnuCL is an OpenCL framework and freely available, open-source software developed at Seoul National University. It naturally extends the original OpenCL semantics to the heterogeneous cluster environment. The target cluster consists of a single host node and multiple compute nodes. They are connected by an interconnection network, such as Gigabit and InfiniBand switches. The host node contains multiple CPU cores and each compute node consists of multiple CPU cores and multiple GPUs. For such clusters, SnuCL provides an illusion of a single heterogeneous system for the programmer. A GPU or a set of CPU cores becomes an OpenCL compute device. SnuCL allows the application to utilize compute devices in a compute node as if they were in the host node. Thus, with SnuCL, OpenCL applications written for a single heterogeneous system with multiple OpenCL compute devices can run on the cluster without any modifications. SnuCL achieves both high performance and ease of programming in a heterogeneous cluster environment.
SnuCL consists of SnuCL runtime and compiler. The SnuCL compiler is based on the OpenCL C compiler in SNU-SAMSUNG OpenCL framework. Currently, the SnuCL compiler supports x86, ARM, and PowerPC CPUs, AMD GPUs, and NVIDIA GPUs.
October 20th, 2011
In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P).
Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures.
(Pennycook, S.J., Hammond, S.D., Mudalige, G.R., Wright, S.A. and Jarvis, S.A.: “On the Acceleration of Wavefront Applications using Distributed Many-Core Architectures”, The Computer Journal (in press) [DOI] [PREPRINT])
October 19th, 2011
The new version 3.1 of rCUDA (Remote CUDA), the Open Source package that allows performing CUDA calls to remote GPUs, is now available. Release highlights:
- Fully updated API to CUDA 4.0 (added support for modules “Peer Device Memory Access” and “Unified Addressing”).
- Fixed low level Surface Reference management functions.
For further information, please visit the rCUDA webpage at http://www.gap.upv.es/rCUDA.
July 17th, 2011
A paper detailing several possible avenues to expand MPI to accelerators has just been presented at “Architectures and System for Big Data (ASBD) 2011″, a workshop at PACT 2011. The abstract and a link to the paper are both below. We (the authors) are looking for feedback as to which options seem attractive to GPU programmers and developers. We welcome any comments/thoughts/critiques you might have.
Current trends in computing and system architecture point towards a need for accelerators such as GPUs to have inherent communication capabilities. We review previous and current software libraries that provide pseudo-communication abilities through direct message passing. We show how these libraries are beneficial to the HPC community, but are not forward-thinking enough. We give motivation as to why MPI should be extended to support these accelerators, and provide a road map of achievable milestones to complete such an extension, some of which require advances in hardware and device drivers.
(Jeff A. Stuart, Pavan Balaji and John D. Owens, “Extending MPI to Accelerators”, PACT 2011 Workshop Series: Architectures and Systems for Big Data, October 2011. [WWW])
June 14th, 2011
A new alpha release of rCUDA 3.0 (Remote CUDA), the Open Source package that allows performing CUDA calls to remote GPUs, has been released. Major improvements included in this new version are:
- Partially updated API to 4.0
- Added compatibility support with CUDA 4.0 environment
- Updated CUBLAS API to 4.0 for the most common CUBLAS routines
- Fixed some bugs
- General performance improvements
For further information, please visit the rCUDA webpage.
November 27th, 2010
Computing in Science & Engineering seeks articles for a May/June 2012 special issue focusing on the use of GPUs in science and engineering applications. Contributions covering all aspects of using GPUs for solving challenging computational science problems are welcome. Of special interest are articles presenting results of porting efforts of large-scale scientific applications on large-scale GPU-based high-performance computers. See the full call for papers for complete details.
November 22nd, 2010
A new major release of rCUDA™ (Remote CUDA), the Open Source package that allows performing CUDA calls to remote GPUs, has been released. The major improvements included in the new version are:
- Updated API to 3.1
- Server now uses Runtime API when possible (CUDA >= 3.1 required)
- Introduced support for the most common CUBLAS routines
- Fixed some bugs
- Added AF_UNIX sockets support to enhance performance on local executions
- Added some load balancing capabilities to the server
- General performance improvements
- Officially added Fermi support
Further information is available from the rCUDA™ webpages http://www.gap.upv.es/rCUDA and http://www.hpca.uji.es/rCUDA.
From a recent announcement:
We are excited to announce the immediate availability of Cluster GPU Instances for Amazon EC2, a new instance type designed to deliver the power of GPU processing in the cloud. GPUs are increasingly being used to accelerate the performance of many general purpose computing problems. However, for many organizations, GPU processing has been out of reach due to the unique infrastructural challenges and high cost of the technology. Amazon Cluster GPU Instances remove this barrier by providing developers and businesses immediate access to the highly tuned compute performance of GPUs with no upfront investment or long-term commitment.
Learn more about the new Cluster GPU instances for Amazon EC2 and their use in running HPC applications.
Also, community support is becoming available; see for instance this blog post about SCG-Ruby on EC2 instances.