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The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan Kaufmann, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

In case you haven’t realized it, the new trend in computer chip technology is multi-core. This is where most of the speed improvements moving forward will come from on our computers. To take full advantage of this however it is necessary to design your applications using Parallel Programming practices, also known as "parallelism". In today’s episode, we will meet with Stephen Toub, who will share with us some of the overarching concepts associated with parallelism, and some of the ways we are trying to empower developers to develop applications to take advantage of it.

Xilinx Inc. today introduced the architecture for a new Extensible Processing Platform they claim will deliver unrivaled levels of system performance, flexibility and integration to developers of a wide variety of embedded systems. The ARM Cortex-A9 MPCore processor-based platform enables system architects and embedded software developers to apply a combination of serial and parallel processing to address the challenges they face in designing today's embedded systems, which must meet ever-growing demands to perform highly complex functions. The Xilinx Extensible Processing Platform offers embedded systems designers a processor-centric design and development approach for achieving the compute and processing horsepower required to drive tasks involving high-speed access to real-time inputs, high-performance processing and complex digital signal processing - or any combination thereof - needed to meet their application-specific requirements, including lower cost and power.

The MATLAB team at MathWorks tested performance scaling of the backslash ("\") matrix division operator to solve for x in the equation A*x = b. In their testing, matrix A occupies far more memory (290 GB) than is available in a single high-end desktop machine—typically a quad core processor with 4-8 GB of RAM, supplying approximately 20 Gigaflops. Therefore, they spread the calculation across machines. In order to solve linear systems of equations they need to be able to access all of the elements of the array even when the array is spread across multiple machines. This problem requires significant amounts of network communication, memory access, and CPU power. They scaled up to a cluster in EC2, giving them the ability to work with larger arrays and to perform calculations at up to 1.3 Teraflops, a 60X improvement. They were able to do this without making any changes to the application code. Here's a graph showing the near-linear scalability of an EC2 cluster across a range of matrix sizes with corresponding increases in cluster size for MATLAB's parallel backslash operator:

Researchers at North Carolina State University have developed a new approach to software development that will allow common computer programs to run up to 20 percent faster and possibly incorporate new security measures. The researchers have found a way to run different parts of some hard-to-parallelize programs — such as word processors and web browsers — at the same time, which makes the programs operate more efficiently.