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Standard Performance Evaluation Corporation

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The Standard Performance Evaluation Corporation (SPEC) is a non-profit corporation formed to establish, maintain and endorse standardized benchmarks and tools to evaluate performance and energy efficiency for the newest generation of computing systems. SPEC develops benchmark suites and also reviews and publishes submitted results from our member organizations and other benchmark licensees.

What's New:

June 14, 2022: Check out the latest updates in the SPEC blog, including the new SPECviewperf 2020 V3.0 with Linux support and a look at what's ahead for SPEC.

April 11, 2022: SPEC has announced the winners of the 2022 SPEC Achievement Awards.

April 6, 2022: The April 2022 issue of the SPEC Research Group newsletter is now available. This regular publication provides information on the latest developments, news, and announcements relevant to the benchmarking and quantitative system evaluation communities.

March 22, 2022: SPECapc releases new SPECapc for 3ds Max 2020 benchmark for workstations running Autodesk 3ds Max 2020. The new benchmark replaces SPECapc for 3ds Max 2015 benchmark and includes 43 tests for comprehensive measurement of CPU and GPU performance with modeling, interactive graphics and visual effects.

March 18, 2022: André Bauer from the University of Würzburg has been recognized as the winner of the 2021 Kaivalya Dixit Distinguished Dissertation Award for his work, "Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases." The award will be presented at the 13th Annual ACM/SPEC International Conference on Performance Engineering (ICPE) conference in April 2022.

March 17, 2022: The 13th ACM/SPEC International Conference on Performance Engineering (ICPE) will be virtual this year and is open for registration. ICPE is also included for the first time in the CORE ranking with rank B. More details are here.

March 10, 2022: SPEC has announced the formation of the SPEC Machine Learning Committee. The new committee will develop practical methodologies for benchmarking artificial intelligence (AI) and machine learning (ML) performance in the context of real-world platforms and environments.

[Older items can be found in the archive.]