“Acceleware offers industry leading training courses for software developers looking to increase their skills in writing or optimizing applications for highly parallel processing. The training focuses on using GPUs for computing and the associated popular programming languages.
The courses are all taught by experienced programmers who provide real world experience, derived from Acceleware’s 9 years of building commercial GPU applications.
Clients will access our top rated training techniques for parallel programming.
We offer public and private courses (private courses require a minimum of 6 students). The 2015 training schedule for public courses is posted on the Acceleware website.”
Acceleware’s Training Courses 2015
General-purpose multiprocessors (as, in our case, Intel IvyBridge and Intel Haswell) increasingly add GPU computing power to the former multicore architectures. When used for embedded applications (for us, Synthetic aperture radar) with intensive signal processing requirements, they must constantly compute convolution algorithms, such as the famous Fast Fourier Transform. Due to its ”fractal” nature (the typical butterfly shape, with larger FFTs defined as combination of smaller ones with auxiliary data array transpose functions), one can hope to compute analytically the size of the largest FFT that can be performed locally on
an elementary GPU compute block. Then, the full application must be organized around this given building block size. Now, due to phenomena involved in the data transfers between various memory levels across CPUs and GPUs, the optimality of such a scheme is only loosely predictable (as communications tend to overcome in time the complexity of computations). Therefore a mix of (theoretical) analytic approach and (practical) runtime validation is here needed. As we shall illustrate, this occurs at both stage, first at the level of deciding on a given elementary FFT block size, then at the full application level.
Mohamed Amine Bergach, Emilien Kofman, Robert de Simone, Serge Tissot, Michel Syska. Efficient FFT mapping on GPU for radar processing application: modeling and implementation. arXiv:1505.08067 [cs.MS]
At the moment Google does not support OpenCL™ as part of the Android platform. However newer generation devices do support it. But not all devices are equipped with the right drivers.
More and more device manufacturers include these drivers as OpenCL™ can be very useful to accelerate specific workloads. The goal of this tool is to build a database of all OpenCL™ capable devices and its properties so developer/users can search though this data. This enables them to see how many devices have OpenCL™ support and what features are implemented. It enables a developer to decide if it make sense for them to utilize OpenCL™ to accelerate their application.
With the tool it is possible to browse through the database and see all devices that support OpenCL. Next to the it is possible to view all the OpenCL capabilities of your current device and all the devices in the on-line database. Read the rest of this entry »
As both CPU and GPU become employed in a wide range of applications, it has been acknowledged that both of these processing units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this paper, we survey heterogeneous computing techniques (HCTs) such as workload-partitioning which enable utilizing both CPU and GPU to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler and application level. Further, we review both discrete and fused CPU-GPU systems; and discuss benchmark suites designed for evaluating heterogeneous computing systems (HCSs). We believe that this paper will provide insights into working and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.
Sparsh Mittal and Jeffrey Vetter, “A Survey of CPU-GPU Heterogeneous Computing Techniques”, accepted in ACM Computing Surveys, 2015. WWW
Developers have been using utility tools such as CPU-Z, GPU-Z, CUDA-Z, OpenCL-Z for a long time. These tools provide platform and hardware information in details and help developers quickly understand the hardware capabilities.
Recently, OpenCL has been supported by most of the latest mobile phones/tablets, as the mobile GPUs are gaining more compute power. OpenCL-A Android can help developer to quickly detect the availability of the OpenCL on a device, and get information about OpenCL-capable platform and devices.
In addition to detecting the OpenCL capability and getting device information, the OpenCL-Z Android is also able to measure the raw compute power in terms of ALU peak GFLOPS performance and memory bandwidth performance. These numbers would be useful for developers who want to take advantage of GPU compute capability of the modern GPU. The developers can roughly predict the performance of a certain algorithm targeting on a specific platform, or compare the raw compute performance among platforms.
The OpenCL-Z Android is a free software and it is now available on Google Play:
Download link at Google Play
The major features of OpenCL-Z Android:
– detect OpenCL availability;
– detect OpenCL driver library;
– display detailed OpenCL platform information;
– display detailed OpenCL device information;
– measure the raw compute performance and memory system bandwidth;
– export OpenCL information to sdcard;
– share OpenCL information with other applications, such as e-mail clients, note applications, social media and so on.
The OpenCL-Z Android has been tested on mobile devices with Qualcomm Snapdragon 8064, 8974, 8084, 8994 chipsets (with Adreno 305, 320, 330, 420, 430 GPUs), Samsung Exynos 5420, 5433 chipsets (with Mali T628, T760 GPUs), MediaTek MT6752 chipset (with Mali T760 GPU), Rockchip RK3288 (with Mali T764 GPU).
The OpenCL-Z Android should be able to support other chipsets. If your device is known to have OpenCL support, but this tool fails to detect it, please contact the developer of OpenCL-Z.
The author of OpenCL-Z is also trying to create a relatively complete list of mobile devices that support OpenCL, the list can be found at the OpenCL-Z official website . If you see any device supporting OpenCL not on that list, please send the author an email and help the list grow.
Stanford, CA – 21 April 2015. The organisers of IWOCL (“eye-wok-ul”), the International Workshop on OpenCL, today announced that AMD and HP have sponsored the Advanced Hands-On OpenCL Tutorial that will kick-off IWOCL 2015. The tutorial, which will focus on advanced OpenCL concepts, is an extension of the highly successful ‘Hands on OpenCL’ course which has received over 3,000 downloads. Simon McIntosh-Smith, Senior Lecturer in High Performance Computing and Architectures at the University of Bristol and one of the authors of the original open-source course will lead the tutorial.
The full-day Advanced Hands-On OpenCL tutorial takes place on Monday 11th May at the Li Ka Shing Center, Stanford University. Registration is $145. For additional information visit: http://www.iwocl.org/conf-2015/handsonopencl-tutorial/ Read the rest of this entry »
Acceleware’s next OpenCL course takes place in Calgary. This professional four day course is designed for programmers who are looking to develop comprehensive skills in writing and optimizing applications that fully leverage data parallel processing capabilities of GPUs. Register before May 12 if you would like to reserve a spot. To find out what the course includes visit:
Learn OpenCL in Calgary www.acceleware.com
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 1.0 version of the PARALUTION Library supports multi-node and multi-GPU configuration via MPI. All iterative solvers support global operations (i.e. distributed matrices and vectors) and all preconditioners can be used in a block-Jacobi fashion locally on each node/GPU. In addition, the software provides a global (fully distributed) Pair-Wise AMG solver. Read the rest of this entry »
After four pre-releases, the stable 2.0.0 version of cf4ocl, the C Framework for OpenCL, is now available.
Since the last beta release, a number of tests were added, and a few bug fixes have been fixed. Support for device fission and native kernels has also been implemented. A complete list of features and fixes is available at https://github.com/FakenMC/cf4ocl/releases.
Cf4ocl has been tested on Linux, OS X and Windows, and offers a pure C object-oriented framework for developing and benchmarking OpenCL projects in C. It aims to:
1. Promote the rapid development of OpenCL host programs in C (with support for C++) and avoid the tedious and error-prone boilerplate code usually required. Read the rest of this entry »
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