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bruun.co | ||
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eregon.me
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| | | | | In this blog post we benchmark many Ruby versions and the latest Ruby Just-in-Time compilers (JITs) on the newest Ruby benchmark suite, yjit-bench. As a teaser, the geometric mean speedups compared to CRuby 3.1 on these 14 benchmarks are: MJIT 1.26x, YJIT 1.39x, JRuby 1.86x and TruffleRuby 6.23x. Read on to find more about the benchmarks and gain insights on these speedups. This blog post is also available on Medium. | |
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www.cppstories.com
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| | | | | After I finished my last post about a performance timer, I got a comment suggesting other libraries - much more powerful than my simple solution. Let's see what can be found in the area of benchmarking libraries. Intro The timer I've introduced recently is easy to use, but also returns just the basic information: elapsed time for an execution of some code... What if we need more advanced data and more structured approach of doing benchmarks in the system? | |
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werat.dev
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| | | | | Benchmarks are often underestimated and don't get the same attention as tests. However "performance is a feature" and when something is not tested it might as well be just broken. If the performance is not measured/tracked regressions are inevitable. Modern tooling makes it really easy to write benchmarks. Some languages have built-in support, for example, Rust comes with cargo bench (docs) and Go has go test -bench (docs). For C++ there is google/benchmark - not as streamlined as having it built into the language infrastructure, but still definitely worth the effort. | |
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blog.skylight.io
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| | | One of the coolest features of Rust is how it automatically manages resources for you, while still guaranteeing both safety (no segfaults) and high performance. Because Rust is a different kind of programming language, it might be difficult to understand what I mean, so let me be perfectly clear: * In | ||
