By Jung W. Suh, Youngmin Kim

Past simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This usually calls for that MATLAB codes run speedier through leveraging the allotted parallelism of pics Processing devices (GPUs). whereas MATLAB effectively offers high-level features as a simulation instrument for fast prototyping, the underlying information and data wanted for using GPUs make MATLAB clients hesitate to step into it. Accelerating MATLAB with GPUs bargains a primer on bridging this gap.

Starting with the fundamentals, constructing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complicated issues comparable to CUDA libraries. The authors proportion their event constructing algorithms utilizing MATLAB, C++ and GPUs for big datasets, enhancing MATLAB codes to raised make the most of the computational energy of GPUs, and integrating them into advertisement software program items. during the booklet, they show many instance codes that may be used as templates of C-MEX and CUDA codes for readers' tasks. obtain instance codes from the publisher's web site: http://booksite.elsevier.com/9780124080805/

• exhibits easy methods to speed up MATLAB codes throughout the GPU for parallel processing, with minimum knowledge
• Explains the comparable heritage on undefined, structure and programming for ease of use
• presents easy labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world tasks

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Extra info for Accelerating MATLAB with GPU Computing: A Primer with Examples

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The starvation-freedom property, while clearly desirable, is the least compelling of the three. Later on, we will see practical mutual exclusion algorithms that fail to be starvation-free. These algorithms are typically deployed in circumstances where starvation is a theoretical possibility, but is unlikely to occur in practice. Nevertheless, the ability to reason about starvation is essential for understanding whether it is a realistic threat. The starvation-freedom property is also weak in the sense that there is no guarantee for how long a thread waits before it enters the critical section.

For example, the nodes in graph T 2 are addressed by 0, 1, and 2. The nodes in graph T 3 are denoted by 00, 01, . . , 22, where the highorder digit indicates one of the three subgraphs, and the low-order digit indicates one node within that subgraph. The key to understanding the n-thread labeling algorithm is that the nodes covered by tokens can never form a cycle. As mentioned, two threads can never form a cycle on T 2 , because the shortest cycle in T 2 requires three nodes. How does the label() method work for three threads?

2. He puts food in the yard. 3. He pulls the string and knocks the can down. The state of the can thus reflects the state of the yard. If the can is down, it means there is food and the pets can eat, and if the can is up, it means the food is gone and Bob can put some more out. We check the following three properties: Mutual Exclusion: Bob and the pets are never in the yard together. Starvation-freedom: If Bob is always willing to feed, and the pets are always famished, then the pets will eat infinitely often.

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