# BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statistics

arXiv: https://arxiv.org/abs/2102.10837 · HTML: https://mlsanity.com/paper/2102.10837/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Expectation Propagation for approximate Bayesian inference](https://mlsanity.com/paper/1301.2294/) (2013)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices](https://mlsanity.com/paper/2008.08509/) (2020)
- [Edward: A library for probabilistic modeling, inference, and criticism](https://mlsanity.com/paper/1610.09787/) (2016)
- [Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters](https://mlsanity.com/paper/1909.02119/) (2019)

## Built on by (most cited first, 2 of 2)

- [Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression](https://mlsanity.com/paper/2204.04831/) (2022)
- [SCOPE: Safe Exploration for Dynamic Computer Systems Optimization](https://mlsanity.com/paper/2204.10451/) (2022)

## Abstract (arXiv)

Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system. However, HPC measurements are error-prone due to non determinism (e.g., undercounting due to event multiplexing, or OS interrupt-handling behaviors). In this paper, we present BayesPerf, a system for quantifying uncertainty in HPC measurements by using a domain-driven Bayesian model that captures microarchitectural relationships between HPCs to jointly infer their values as probability distributions. We provide the design and implementation of an accelerator that allows for low-latency and low-power inference of the BayesPerf model for x86 and ppc64 CPUs. BayesPerf reduces the average error in HPC measurements from 40.1% to 7.6% when events are being multiplexed. The value of BayesPerf in real-time decision-making is illustrated with a simple example of scheduling of PCIe transfers.