By Samuel P. Midkiff
ISBN-10: 1608458415
ISBN-13: 9781608458417
Compiling for parallelism is a longstanding subject of compiler study. This e-book describes the elemental ideas of compiling "regular" numerical courses for parallelism. we commence with a proof of analyses that permit a compiler to appreciate the interplay of information reads and writes in several statements and loop iterations in the course of software execution. those analyses contain dependence research, use-def research and pointer research. subsequent, we describe how the result of those analyses are used to permit alterations that make loops extra amenable to parallelization, and talk about differences that disclose parallelism to focus on shared reminiscence multicore and vector processors. We then speak about a few difficulties that come up while parallelizing courses for execution on dispensed reminiscence machines. eventually, we finish with an outline of fixing Diophantine equations and recommendations for additional readings within the issues of this publication to permit the reader to delve deeper into the sector.
desk of Contents: advent and assessment / Dependence research, dependence graphs and alias research / application parallelization / modifications to switch and cast off dependences / Transformation of iterative and recursive constructs / Compiling for allotted reminiscence machines / fixing Diophantine equations / A advisor to additional analyzing
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Extra info for Automatic Parallelization: An Overview of Fundamental Compiler Techniques
Example text
Logically, dependence testing has two phases. The dependence test driver determines pairs of references to be tested, and the direction vector under which they will be tested. It then calls a dependence test to actually perform the test. 1 DETERMINING REFERENCES TO TEST FOR DEPENDENCE The dependence test driver operates over all pairs of accesses within some selected loop i, where i is typically the outermost loop in a loop nest that is being analyzed. The driver then examines all references which may access the same storage.
Depth of the loop), but provides faster solutions than other techniques [119, 134] that utilize Fourier-Motzkin elimination to prove independence. When originally developed it was too slow for general use, but with modern machines can be applied to those references that may be dependent after applying Banerjee’s inequalities. The Power Test is exact for loops whose bounds are affine functions of outer loop indices, and in which all constants in the dependence equations and loop bounds are known.
There are four kinds of data dependence, three of which are commonly considered in loop parallelization. A flow or true dependence exists when a value is written to an array element, and then read from that element at a later time. An anti dependence exists when a value is read from an array element, and then written to that element at a later time. An output dependence exists when a value is written to an array element, and then overwritten with a different value at a later time. The fourth type of dependence is the input dependence which occurs across writes.
Automatic Parallelization: An Overview of Fundamental Compiler Techniques by Samuel P. Midkiff
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