Numeric Conversions il = f l f l = il in Java

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Numeric Conversions il = f l f l = il
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Integer operations are fast, except for division and modulus Floating-point operations are as fast or faster, a surprise to people who grew up at a time when floatingpoint operations were much more expensive than integer operations Other basic operations are also quite fast, including function calls, the last three lines of this group:
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Integer Vector Operations v[i] = i v[v[i]] = i v[v[v[i]]] = i Control Structures i f (i == 5) i + 1 + i f (i 5) i l + + != while (i 0) il++ < il = suml(i2) il= sum2(i2, i 3 ) il= sum3(i2, i 3 , i 4 )
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But input and output are not so cheap, nor are most other library functions:
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Input/Output f p u t s ( s , fp) f g e t s ( s , 9 , fp) f p r i n t f ( f p , "%d\nW, i ) f s c a n f (fp "%d", &il) Ma1 1oc f r e e (ma1 1oc (8)) S t r i n g Functions s t r c p y ( s , "0123456789") i l = strcmp(s, s) i l = strcmp(s "a123456789") S t r i ng/Number Conversions i l = a t o i ("12345") sscanf("12345", "%dm,&il) s p r i n t f ( s , "%dm, i ) f l = atof("12345") sscanf("12345", "%f", & f l ) s p r i n t f ( s , "%62fW,12345) 157 176 64 402 2376 1492 4098 6438 3902
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The times for ma1 l o c and f r e e are probably not indicative of true performance, since freeing immediately after allocating is not a typical pattern Finally, math functions: Math Functions i l = rand() f l = log(f2) f l = exp(f2) f l = sin(f2) f l = sqrt(f2) These values would be different on different hardware, of course, but the trends can be used for back-of-the-envelope estimates of how long something might take, or for comparing the relative costs of 110 versus basic operations, or for deciding whether to rewrite an expression or use an inline function There are many sources of variability One is compiler optimization level Mode m compilers can find optimizations that elude most programmers Furthermore, current CPUs are so complicated that only a good compiler can take advantage of their ability to issue multiple instructions concurrently, pipeline their execution, fetch instructions and data before they are needed, and the like Computer architecture itself is another major reason why performance numbers are hard to predict Memory caches make a great difference in speed, and much cleverness in hardware design goes into hiding the fact that main memory is quite a bit slower than cache memory Raw processor clock rates like "400 MHz" are suggestive but don't tell the whole story; one of our old 200 MHz Pentiums is significantly slower than an even older 100 MHz Pentium because the latter has a big second-level cache and the former has none And different generations of processor, even for the
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Exercise 7-6 Create a set of tests for estimating the costs of basic operations for computers and compilers near you, and investigate similarities and differences in performance Exercise 7-7 Create a cost model for higher-level operations in C++ Among the features that might be included are construction, copying, and deletion of class objects; member function calls; virtual functions; inline functions; the iostream library; the STL This exercise is open-ended, so concentrate on a small set of representative operations Exercise 7-8 Repeat the previous exercise for Java
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77 Summary
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Once you have chosen the right algorithm, performance optimization is generally the last thing to worry about as you write a program If you must undertake it, however, the basic cycle is to measure, focus on the few places where a change will make the most difference, verify the correctness of your changes, then measure again Stop as soon as you can, and preserve the simplest version as a baseline for timing and correctness When you're trying to improve the speed or space consumption of a program, it's a good idea to make up some benchmark tests and problems so you can estimate and keep track of performance for yourself If there are already standard benchmarks for your task, use them too If the program is relatively self-contained, one approach is to find or create a collection of "typical" inputs; these might well be part of a test suite as well This is the genesis of benchmark suites for commercial and academic systems like compilers, computers, and the like For example, Awk comes with about 20 small programs that together cover most of the commonly-used language features; these programs are run over a very large input file to assure that the same results are computed and that no performance bug has been introduced We also have a collection of standard large data files that can be used for timing tests In some cases it might help that such files have easily verified properties, for example a size that is a power of ten or of two Benchmarking can be managed with the same kind of scaffolding as we recommended for testing in 6 Timing tests are run automatically; outputs include enough identification that they can be understood and replicated; records are kept so that trends and significant changes can be observed By the way, it's extremely difficult to do good benchmarking, and it is not unknown for companies to tune their products to show up well on benchmarks so it is wise to take all benchmark results with a grain of salt
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