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.
A new open-source CFD project have just been published. RapidCFD is a new open-source CFD project that uses NVIDIA CUDA for the entire calculation process which gives a significant reduction in computation time.
- most incompressible and compressible solvers on static mesh are available
- all the calculations are done on the GPU
- no overhead for GPU-CPU memory copy
- can run in parallel on multiple GPUs
Visit RapidCFD project page.
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 »
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.
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
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.
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
The Cf4ocl project is a GPLv3/LGPLv3 initiative to provide an object-oriented interface to the OpenCL C API with integrated profiling, promoting the rapid development of OpenCL host programs and avoiding boilerplate code. Its main goal is to allow developers to focus on OpenCL device code. After two alpha releases, the first beta is out, and can be tested on Linux, Windows and OS X. The framework is independent of the OpenCL platform version and vendor, and includes utilities to simplify the analysis of the OpenCL environment and of kernel requirements. While the project is making progress, it doesn’t yet offer OpenGL/DirectX interoperability, support for sub-devices, and doesn’t support pipes and SVM.
Cf4ocl can be downloaded from http://fakenmc.github.io/cf4ocl/.