benchmarking – Trivial mathematical problems as language benchmarks – Education Career Blog

Why do people insist on using trivial mathematical problems like finding numbers in the Fibonacci sequence for language benchmarks? Don’t these usually get optimized to relativistic speeds? Isn’t the brunt of the bottlenecks usually in I/O, system API calls, operations on strings and structures, processing large quantities of data, abstract object-oriented stuff, etc?

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It is a throwback to the old days, when compiler technology for what we would now call basic math was still evolving rapidly.

Now, compiler evolution is more focused on exploiting new instructions for niche operations, 64-bit math, and so on.

Micro-benchmarks such as the ones you mention were useful, though, when evaluating the efficiency of the hotspot compiler when Java was first launched, and in evaluating the efficiency of .NET versus C/C++.

Your suggestion that I/O and system calls are the likely bottlenecks is correct, at least for some space of problems. But I notice you suggested string operations. One person’s irrelevant micro-benchmark is another person’s critical performance metric.

EDIT: ps, I also remember using linpack and other micro-benchmarks to compare versions of the JVM, and to compare vendors of the JVM. From v4 to v5 there was a big jump in perf, I guess the JIT compiler got more effective. Also, IBM’s JVM was ahead of Sun’s at that time, on Windows-x86.

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Because if you want to benchmark the language/compiler, these “math problems” are good indicators of the “bare speed” of the generated code. Either they use the iterative solution, which is a tight loop and indicates how well can the compiler push the instructions to the processor, or they use the recursive solution, which indicates how does it handle recursive calls of short functions (inlining, tail-recursion etc.) (although the Ackermann function is usually used for that too).

Usually, the benchmark suite for the language contain tests benchmarking other parts as well – eg. gzip compression, text searching, object creation, virtual function call, exception throw/catch benchmarks.

The other things you’ve noticed, syscalls and IO are usually not included because

  • syscalls are in fact not that slow – applications don’t spend significant porion of the time in the kernel, except for test specifically targeted at them or when something is seriously wrong with the program

  • syscall and IO performance does not depend on the language, but rather on the OS & hardware

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I’d think a simple, well-established algorithm would remove the possibility that the benchmark is biased (whether through ignorance or malice) to favor one language. It is very difficult to write a complex program in two different languages exactly the same. Testing something like the efficiency of a multithreaded application in c# vs java, for example, would require developers skilled in multithreaded development both languages, and there would still be questions as to whether the benchmark app properly represents the general case, or if it is misrepresenting a special case that only one language handles well.

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Back when the sieve of eratosthanes was a popular benchmark for C compilers, I thought it would be funny if one of the compiler authors would recognize the sieve code and replace it with a pre-computed lookup.

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