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Digital Signal Processing, Data Analytics, Big Data, HPC, Deep Learning, GPGPU
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OpenCL™ 2.0 is here! Download the Release 2 of Intel® SDK for OpenCL™ Applications 2014

Dear Developers,

We are happy to announce the availability of our latest and most advanced SDK for OpenCL: Release 2 of Intel® SDK for OpenCL SDK 2014 is the industry’s first SDK to provide an OpenCL 2.0 development environment with the new Intel® Core™ M Processors.

This major advance in graphics programmability and accessibility will help you make greater use of the graphics engine to deliver new experiences on Intel-based platforms 

New with SDK 2014 Release 2:
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Khronos Announces OpenCL SPIR 2.0

Khronos Announces OpenCL SPIR 2.0 | EEDSP | Scoop.it

Khronos released OpenCL SPIR 1.2 as a provisional specification, keeping it there over a protracted period to solicit feedback over the first version of the standard. Since that provisional release, Khronos finalized OpenCL 1.2 SPIR in early 2014 and has been working on building up their developer and user bases for SPIR.

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Kyle Lutz: Boost.Compute v0.3 Released

Boost.Compute is a header-only C++ library for GPGPU and parallel-computing based on OpenCL. It is available on GitHub and instructions for getting started can be found in the documentation.

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NVIDIA GPUs Deliver a Shot in the ARM for HPC Industry

NVIDIA GPUs Deliver a Shot in the ARM for HPC Industry | EEDSP | Scoop.it
Among the most interesting announcements at this week’s ISC’14 is the emergence of a new class of system – one that marries the many advantages of ARM processors with the massively parallel processing power of NVIDIA Tesla GPU accelerators. This is great news for the industry. Initially designed for micro-servers and web servers, ARM64 server processors… Read More
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Managing GPUs by SLURM - insideHPC

In this video from the HPC Advisory Council Swiss Conference 2014, Massimo Benini from CSCS presents: Managing GPUs by SLURM.
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CUDA 6, Available as Free Download, Makes Parallel Programming Easier, Faster

CUDA 6, Available as Free Download, Makes Parallel Programming Easier, Faster | EEDSP | Scoop.it

Available now to all developers on the CUDA website, the CUDA 6 Release Candidate is packed with several new features that are sure to please developers

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NVIDIA Announces CUDA 6: Unified Memory for CUDA

NVIDIA Announces CUDA 6: Unified Memory for CUDA | EEDSP | Scoop.it

The big news here – and the headlining feature for CUDA 6 – is that NVIDIA has implemented complete unified memory support within CUDA. The toolkit has possessed unified virtual addressing support since CUDA 4, allowing the disparate x86 and GPU memory pools to be addressed together in a single space. But unified virtual addressing only simplified memory management; it did not get rid of the required explicit memory copying and pinning operations necessary to bring over data to the GPU first before the GPU could work on it.

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Towards Performance-Portable, Scalable and Convenient Linear Algebra :: GPGPU.org

The rise of multi- and many-core architectures also gave birth to a plethora of new parallel programming models. Among these, the open industry standard OpenCL addresses this heterogeneity of programming environments by providing a unified programming framework. The price to pay, however, is that OpenCL requires additional low-level boilerplate code, when compared to vendor-specific solutions, even if only simple operations are to be performed. Also, the unified programming framework does not automatically provide any guarantees on performance portability of a particular implementation. Thus, device-specific compute kernels are still required for obtaining good performance across different hardware architectures.

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High Scalability - High Scalability - Strategy: Stop Using Linked-Lists

High Scalability - High Scalability - Strategy: Stop Using Linked-Lists | EEDSP | Scoop.it
What data structure is more sacred than the link list? If we get rid of it what silly interview...
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Intel® SDK for OpenCL* Applications – Frequently Asked Questions | Intel® Developer Zone

Intel® SDK for OpenCL* Applications – Frequently Asked Questions | Intel® Developer Zone | EEDSP | Scoop.it
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Intel® SDK for OpenCL* Applications 2013 Release Notes | Intel® Developer Zone

Intel® SDK for OpenCL* Applications 2013 Release Notes | Intel® Developer Zone | EEDSP | Scoop.it

Intel® SDK for OpenCL* Applications 2013 is a comprehensive software development environment for OpenCL applications on the 3rd and the future 4th Generation Intel® Core™ processors, which support OpenCL 1.2 on Windows 7* and Windows 8* operating systems

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PARALUTION - The Library for Iterative Sparse Methods on CPU and GPU

PARALUTION - The Library for Iterative Sparse Methods on CPU and GPU | EEDSP | Scoop.it

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.

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Unified Memory: Now for CUDA Fortran Programmers

Unified Memory: Now for CUDA Fortran Programmers | EEDSP | Scoop.it
Unified Memory is a CUDA feature that we've talked a lot about on Parallel Forall. CUDA 6 introduced Unified Memory, which dramatically simplifies GPU programming by giving programmers a single poi...
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Accelerate R Applications with CUDA

Accelerate R Applications with CUDA | EEDSP | Scoop.it

In this article, I will introduce the computation model of R with GPU acceleration, focusing on three topics:

accelerating R computations using CUDA libraries;calling your own parallel algorithms written in CUDA C/C++ or CUDA Fortran from R; andprofiling GPU-accelerated R applications using the CUDA Profiler.

 

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Echelon Blog

Echelon Blog | EEDSP | Scoop.it

In preparation for my C++Now talk entitled The Future of Accelerator Programming in C++ I am currently reviewing numerous C++ libraries. I put together a catalogue of questions for these reviews. The questions are intended to gauge scope, use-cases, performance, quality and level of abstraction of each library.

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CUDA Spotlight: GPU-Accelerated Speech Recognition

CUDA Spotlight: GPU-Accelerated Speech Recognition | EEDSP | Scoop.it
This week's Spotlight is on Dr. Ian Lane of Carnegie Mellon University. Ian is an Assistant Research Professor and leads a speech and language processing research group based in Silicon Valley. He ...
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Topiary eDiscovery LLC's curator insight, March 12, 10:30 AM

Carnegie Mellon... the Incubator.

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How New Features in CUDA 6 Make GPU Acceleration Easier - insideHPC

In this video from the Nvidia booth at SC13, Mark Harris from Nvidia presents: New Features in CUDA 6 Make GPU Acceleration Easier.
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New Guide: Dependency Injection with Unity - Grigori Melnik: Thoughts on Agile Software Engineering and Beyond - Site Home - MSDN Blogs

New Guide: Dependency Injection with Unity - Grigori Melnik: Thoughts on Agile Software Engineering and Beyond - Site Home - MSDN Blogs | EEDSP | Scoop.it

dependency injection – a design pattern that allows the removal of hard-coded dependencies and makes it possible to assemble a service by changing dependencies easily, whether at run-time or compile-time. It promotes code reuse and loosely-coupled design which leads to more easily maintainable and flexible code.

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Image Processing with C++ AMP and the .NET Framework - Visual C++ Team Blog - Site Home - MSDN Blogs

Image processing is a computational task that lends itself very well to GPU compute scenarios. In many cases the most commonly used algorithms are inherently massively parallel, with each pixel in the image being processed independently from the others. As a result, image processing toolkits have been early adopters of the new GPGPU programming model.

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OpenCL*: the advantages of heterogeneous approach | Intel® Developer Zone

OpenCL*: the advantages of heterogeneous approach | Intel® Developer Zone | EEDSP | Scoop.it

Along with the rise of General Purpose computing on Graphics Processing Units (GPGPU), GPUs themselves are evolving rapidly from fixed-function rasterization engines to more general processors. Today, discrete GPUs are typically connected to the CPU via the PCI Express* (PCIe) bus, which significantly limits the data transfer rate between the devices. Explicit boundaries for different memory spaces/hierarchies and high latency synchronization between devices result in quite a coarse-grained level of abstraction. Most OpenCL workloads today target the GPU only, leaving the CPU to do mainly scheduling, file and network I/O, and other “host” types of orchestration. In this approach the costs of PCIe transfers might be prohibitive if tasks are small and not amortized well by execution speed of a GPU.

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NVIDIA and Continuum Analytics Announce NumbaPro, A Python CUDA Compiler

NVIDIA and Continuum Analytics Announce NumbaPro, A Python CUDA Compiler | EEDSP | Scoop.it

As NVIDIA’s GPU Technology Conference 2013 kicks off this week, there will be a number of announcements coming down the pipeline from NVIDIA and their partners. The biggest and more important of these announcements will be Tuesday morning with NVIDIA CEO’s Jen-Hsun Huang’s keynote speech, while some other product announcements such as this one are being released today with the start of the show.

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