NeoMPX: Characterizing and Improving Estimation of Multiplexing Hardware Counters for PAPI

Modern processors provide hundreds of low-level hardware events (such as cache miss rate), but offer only a small number (usually 6–12) of hardware counters to collect these events due to limited register resource. Multiplexing (MPX) is an estimation-based technique designed to collect hardware events simultaneously with few hardware counters. However, the low-accuracy of existing MPX methods prevents this technique from wide usage in real conditions. To obtain accurate and reliable hardware counter values, we conducted this work in three steps: 1) to explore the root cause of inaccuracy, we characterized the estimation errors of MPX, and found that estimation errors arise from the outliers in PAPI; 2) to eliminate these outliers and improve MPX accuracy, we proposed two non-linear growth rate gradient estimation methods: divided curved-area method (DCAM) and curved-area method (CAM); 3) based on these two methods, we developed a new MPX library for PAPI, NeoMPX. We evaluated NeoMPX with six Rodinia benchmarks on four mainstream x86 and ARM server processors, and compared the results with PAPI default MPX and two other state-of-art MPX methods, DIRA, and TAM. Evaluations show that, for collecting 16 evaluated hardware events, our methods can improve up to 59% accuracy than PAPI default MPX, and achieve 36% and 5% higher accuracy than DIRA and TAM, respectively. We have open-sourced NeoMPX and expect it to enable PAPI MPX for practical usage.