Panoptes: A Binary Translation Framework for CUDA

May 22nd, 2012

Traditional CPU-based computing environments offer a variety of binary instrumentation frameworks. Instrumentation and analysis tools for GPU environments to date have been more limited. Panoptes is a binary instrumentation framework for CUDA that targets the GPU. By exploiting the GPU to run modified kernels, computationally-intensive programs can be run at the native parallelism of the device during analysis. To demonstrate its instrumentation capabilities, we currently implement a memory addressability and validity checker that targets CUDA programs.

Panoptes traces targeted programs by library interposition at runtime. Read the rest of this entry »

New rCUDA version beta testing

April 18th, 2012

The rCUDA Team is proud to announce a new version of the rCUDA framework which will include many new functionalities as well as boosted performance. This new version, cooked for over a year, will incorporate pipelined transfers, full multi-thread and multi-node capabilities, CUDA 4.1 support, global scheduler integration, support for CUDA C extensions, and native InfiniBand support. A closed beta teting program has been started. See the complete text at

Latest PGI Compilers support OpenACC and CUDA for x86

March 6th, 2012

HPCWire reports:

PORTLAND, Ore., March 5 — The Portland Group, a wholly-owned subsidiary of STMicroelectronics, today announced availability of the 2012 release of the PGI line of high-performance parallelizing compilers and development tools for Linux, OS X and Windows. PGI 2012 is the first general release to include support for the OpenACC directive-based programming model for NVIDIA CUDA-enabled Graphics Processing Units (GPUs). This release is also the first to include the fully feature-enabled PGI CUDA C/C++ compiler for multi-core x64 CPUs from Intel and AMD. In addition, PGI 2012 includes a number of performance and feature enhancements for multi-core x64 processor-based HPC systems.


Chai, a new managed platform for GPGPU

February 13th, 2012

Chai is a new managed platform for GPGPU. It is a free and open source clean room workalike of the PeakStream platform. While not production-ready, the just-released alpha version is able to compile and run non-trivial PeakStream demo code on AMD and NVIDIA GPUs (e.g. conjugate gradient).

Chai combines an application virtual machine, garbage collection, auto-tuning JIT compiler, and high level array programming language implemented as an embedded domain-specific language in C++. The JIT back-end uses expectation-maximization to auto-tune and generate vectorized OpenCL. The JIT includes auto-tuned model families for GEMM and GEMV. Although originally developed for AMD GPUs, these parameterized kernel families also generalize to NVIDIA GPUs.

Performance of SpMV in CUSPARSE, CUSP and SpeedIT

January 14th, 2012

The SpeedIt team recently compared and benchmarked the SpMV performance of CUSPARSE 4.0, CUSP 0.2.0 and SpeedIT 2.0 on 23 randomly chosen matrices from University Florida Matrix Collection. Comparisons were done on a Tesla C2050 in single and double precision. The full report is available at

OpenCL Compiler Tools

October 19th, 2011

OCLTools is a powerful, yet compact, suite of Open Source tools that provide OpenCL developers with more alternatives to kernel compilation. OCLTools enables developers to eliminate costly kernel compilation time from the runtime of your application. With OCLTools developers can embed the source code of their kernels (clear text or encrypted) directly into their program binaries, eliminating the need to distribute kernel source code in the open while still maintaining the flexibility of runtime compilation. Both source code and precompiled binaries can be embedded into OpenCL binaries, effectively eliminating the additional kernel compilation overhead from the run time of your application.

For more information go to

Aparapi – Parallel programming with Java and OpenCL

September 15th, 2011

AMD just released to open source a project called Aparapi that started in their JavaLabs team. Aparapi is an API for expressing data parallel workloads in Java and a runtime component capable of converting the Java bytecode of compatible workloads into OpenCL™ so that it can be executed on a variety of GPU devices.  More information can be found in this blog entry.

Thrust: A Productivity-Oriented Library for CUDA

September 12th, 2011


This chapter demonstrates how to leverage the Thrust parallel template library to implement high-performance applications with minimal programming effort. Based on the C++ Standard Template Library (STL), Thrust brings a familiar high-level interface to the realm of GPU Computing while remaining fully interoperable with the rest of the CUDA software ecosystem. Applications written with Thrust are concise, readable, and efficient.

(Nathan Bell and Jared Hoberock: “Thrust: A Productivity-Oriented Library for CUDA”, GPU Computing Gems, Jade Edition, edited by Wen-mei W. Hwu, October 2011)

GPU.NET v2.0 released

July 29th, 2011

TidePowerd has released Version 2 of their GPU computing solution for the .NET framework, GPU.NET. Their platform allows developers to quickly and easily write GPU-accelerated applications completely in .NET-based languages. Some key benefits include:

  • Stay in C# and treat kernel methods like any regular method
  • “Boilerplate” GPU programming tasks such as memory transfer and GPU scheduling are abstracted from the developer
  • Cross-platform and cross-hardware with a single binary
  • Systems seamlessly adapt to new hardware without rewriting code
  • Speed on par with native code

New version 2 features:

  • Visual Studio Error list and IntelliSense integration
  • On-device random number generation
  • Double precision support

A free 30-days evaluation license is available, as well as in-depth examples and tutorials.

AMD Fusion Developer Summit

March 29th, 2011

Heterogeneous computing is moving into the mainstream, and a broader range of applications are already on the way. As the provider of world-class CPUs, GPUs, and APUs, AMD offers unique insight into these technologies and how they interoperate. We’ve been working with industry and academia partners to help advance real-world use of these technologies, and to understand the opportunities that lie ahead. It’s time to share what we’ve learned so far.

With tutorials, hands-on labs, and sessions that span a range of topics from HPC to multimedia, you’ll have the opportunity to expand your view of what heterogeneous computing currently offers and where it is going. You’ll hear from industry innovators and academic pioneers who are exploring different ways of approaching problems, and utilizing new paradigms in computing to help identify solutions. You’ll meet AMD experts with deep knowledge of hardware architectures and the software techniques that best leverage those platforms. And you’ll connect with other software professionals who share your passion for the future of technology.

Learn more at

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