Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time to solution exhibits high variance, and software and hardware systems are so diverse that fair benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that overcomes these challenges. Our analysis quantitatively evaluates MLPerf's efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors.

Processor-level dynamic thermal management techniques have long targeted worst-case thermal margins. We examine the thermal-performance trade-offs in average-case, preventive thermal management by actively degrading application performance to achieve long-term thermal control. We propose Dimetrodon, the use of idle cycle injection, a flexible, per-thread technique, as a preventive thermal management mechanism and demonstrate its efficiency compared to hardware techniques in a commodity operating system on real hardware under throughput and latency-sensitive real-world workloads. Compared to inflexible hardware techniques, Dimetrodon achieves favorable trade-offs for temperature reductions up to 30% due to rapid heat dissipation during short idle intervals.