Low Overhead Allocation Sampling in a Garbage Collected Virtual Machine

TL;DR

This paper introduces a low-overhead, sampling-based allocation profiler integrated into PyPy's garbage collector, enabling efficient profiling of memory allocations with minimal performance impact.

Contribution

It presents a novel, deeply integrated sampling profiler for allocations in a garbage collected VM, achieving low overhead and tunability.

Findings

Maximum 25% overhead with 4 MB sampling period

Efficient profiling of allocation-heavy programs

Deep integration reduces profiling overhead

Abstract

Compared to the more commonly used time-based profiling, allocation profiling provides an alternate view of the execution of allocation heavy dynamically typed languages. However, profiling every single allocation in a program is very inefficient. We present a sampling allocation profiler that is deeply integrated into the garbage collector of PyPy, a Python virtual machine. This integration ensures tunable low overhead for the allocation profiler, which we measure and quantify. Enabling allocation sampling profiling with a sampling period of 4 MB leads to a maximum time overhead of 25% in our benchmarks, over un-profiled regular execution.