9 edition of Performance Optimization of Numerically Intensive Codes (Software, Environments and Tools) found in the catalog.
March 19, 2001 by Society for Industrial Mathematics .
Written in English
|The Physical Object|
|Number of Pages||173|
The benefit they allow you to gain may be much too insignificant, when compared to general system design optimizations Wirth. Other common trade-offs include code clarity and conciseness. In the current configuration HECToR processors have one individual cache level per core, a second level shared by two cores the so-called module and a third level shared across 8 cores the so-called die. Memory usage logged over time as code segments 1 and 2 execute. Gains are usually limited for local optimization, and larger for global optimizations. This is caused by memory hierarchies which were introduced to reduce average memory access times.
Some examples include Out-of-order executionSpeculative executionInstruction pipelinesand Branch predictors. Tanenbaum makes it sound convincing. Excessive hand-tuning of the source code can hide the original intent of the procedure and could have a significantly detrimental effect on performance for different architectures. There are other ways to eliminate subprogram calls: Use statement functions. Cache effects are data-size- and machine-dependent, making it difficult to predict performance. Bottlenecks[ edit ] Optimization may include finding a bottleneck in a system — a component that is the limiting factor on performance.
Focusing on just those parts of the program that use most of the compute time, you might consider the following strategies: Replace handwritten procedures with calls to equivalent optimized libraries. As a consequence, programmers and compilers don't always take advantage of the more efficient instructions provided by newer CPUs or quirks of older models. Eliminating Subprogram Calls Subroutines called deep within a loop nest could be called thousands of times. In one step, code segment 2 preallocates the entire array x to the largest size that it needs to be. More complex algorithms and data structures perform well with many items, while simple algorithms are more suitable for small amounts of data — the setup, initialization time, and constant factors of the more complex algorithm can outweigh the benefit, and thus a hybrid algorithm or adaptive algorithm may be faster than any single algorithm.
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A simple example that shows the importance of spatial locality is the order of the nested loops over matrix elements. Machine B is a Dual 2. Optimizing a whole system is usually undertaken by programmers because it is too complex for automated optimizers.
Platform dependent and independent optimizations[ edit ] Code optimization can be also broadly categorized as platform -dependent and platform-independent techniques.
On the other hand, platform-dependent techniques involve instruction scheduling, Performance Optimization of Numerically Intensive Codes book parallelismdata-level parallelism, cache optimization techniques i.
After the programmer is reasonably sure that the best algorithm is selected, code optimization can start. According to the above choices this guide presents optimisation as a problem of Performance Optimization of Numerically Intensive Codes book and library selection in the next section, followed by a presentation of the key factors that must be considered when writing numerically intensive code in the subsequent sections.
Anderson, W. When the bottleneck is localized, optimization usually starts with a rethinking of the algorithm used in the program.
A "fully optimized" program might be more difficult to comprehend and hence may contain more faults than unoptimized versions. Higham, B. Most programmers don't complain about optimizations performed by the optimizer, some of which are conventional and obligatory.
Of all three techniques, this familiar one can give the biggest performance improvement. If the algorithm permits, you can also maximize cache efficiency by using the single index method, x kinstead of x r,c.
Memory Performance Glossary Cache. This is a very good book that is however slightly outdated and unfortunately out of print. Code whose performance is limited by memory speed, not by CPU speed.
Other common trade-offs include code clarity and conciseness. Figure 4. A general technique to improve performance is to avoid work. Choice of platform and programming language occur at this level, and changing them frequently requires a complete rewrite, though a modular system may allow rewrite of only some component — for example, a Python program may rewrite performance-critical sections in C.
Google Scholar  S. Before starting it is very important to estimate the possible performance gain from a performance analysis which can identify the code sectors worth an optimisation effort and what sort of optimisation is needed e. Accessing data stored on disk is orders of magnitude slower than accessing data stored in RAM, however.Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.
1 Performance optimization of numerically intensive codes.
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