Read e-book online Accelerating MATLAB with GPU Computing: A Primer with PDF

By Jung W. Suh, Youngmin Kim

ISBN-10: 0124080804

ISBN-13: 9780124080805

Past simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This frequently calls for that MATLAB codes run speedier by means of leveraging the dispensed parallelism of photographs Processing devices (GPUs). whereas MATLAB effectively presents 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 deals a primer on bridging this gap.

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

• indicates the right way to speed up MATLAB codes during the GPU for parallel processing, with minimum knowledge
• Explains the comparable heritage on undefined, structure and programming for ease of use
• offers 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

Example text

So far, the language only has the capacity to handle Integer literals and applications of Plus. The function parseExpr is the parser for the language, taking a String and converting it into a tuple: the first element being the Abstract Syntax Tree for Expr, and the second the unconsumed input. To show this in practice, the following shows how the parser and evaluator can be invoked from the GHCi command line: Prelude Parser> parseExpr "+ 1 2" (Plus (Literal 1) (Literal 2),"") Prelude Parser> eval $ fst $ parseExpr "+ 1 2" 3 For reasons of simplicity, the language does not include parentheses (although this could easily be integrated into future versions) and + is not applied as an infix function, also the expressions only take unsigned (positive) integers.

8. The evaluator implementation with the generality of binary operators expressed data Expr = ... | LetExp String Expr | Var String ... addedLetExp = error "Added LetExp String Expr Expr to Expr" addedVar = error "Added Var String to Expr" ... eval :: Expr -> Int ... eval (LetExp p_1 p_2 p_3) = addedLetExp eval (Var p_1) = addedVar The Environment type, addEnv and lookup functions are now defined (not part of the refactoring sequence). Finally the definition of eval needs to be modified to take a new argument, namely the Environment.

Small-step operational semantics rules Typing Coroutines 21 suspended coroutine (the callee), becoming its caller (E-Res). In doing so, it enters the waiting state, and the callee becomes running. A running coroutine can also yield, after which it is suspended and the caller running (E-Yie). If a running coroutine reduces to a value, it is said to return that value. The returning coroutine enters its terminal state, and the value is then passed to the caller if there is one (E-CoRet) or becomes the final result of the program.

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Accelerating MATLAB with GPU Computing: A Primer with Examples by Jung W. Suh, Youngmin Kim


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