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Programming language implementers rely heavily on benchmarking for measuring and understanding performance of algorithms, architectural designs, and trade-offs between alternative implementations of compilers, runtime systems, and virtual machine components. Given this fact, it seems a bit ironic that it is often more difficult to come up with a good benchmark suite than a good implementation of a programming language.
This paper presents the main aspects of the design and the current status of bencherl, a publicly available scalability benchmark suite for applications written in Erlang. In contrast to other benchmark suites, which are usually designed to report a particular performance point, our benchmark suite aims to assess *scalability*, i.e., help developers to study a set of performance points that show how an application's performance changes when additional resources (e.g., CPU cores, schedulers, etc.) are added. We describe the scalability dimensions that the suite aims to examine and present its infrastructure and current set of benchmarks. We also report some limited set of performance results in order to show the capabilities of our suite.
Programming language implementers rely heavily on benchmarking for measuring and understanding performance of algorithms, architectural designs, and trade-offs between alternative implementations of compilers, runtime systems, and virtual machine components. Given this fact, it seems a bit ironic that it is often more difficult to come up with a good benchmark suite than a good implementation of a programming language.
This paper presents the main aspects of the design and the current status of \bencherl, a publicly available scalability benchmark suite for applications written in Erlang. In contrast to other benchmark suites, which are usually designed to report a particular performance point, our benchmark suite aims to assess \emph{scalability}, i.e., help developers to study a set of performance points that show how an application's performance changes when additional resources (e.g., CPU cores, schedulers, etc.) are added. We describe the scalability dimensions that the suite aims to examine and present its infrastructure and current set of benchmarks. We also report some limited set of performance results in order to show the capabilities of our suite.", // position: { // target: 'mouse' // } //}); </script>