PALMED: Throughput Characterization for Superscalar Architectures -- Extended Version

TL;DR

This paper introduces Palmed, a tool that automatically constructs resource mappings for superscalar CPU architectures using runtime measurements, enabling accurate throughput modeling without hardware counters.

Contribution

Palmed provides a novel, resource-based abstraction for throughput characterization, simplifying port mapping problems and matching the accuracy of existing models.

Findings

Palmed achieves sub-10% mean square error in throughput prediction.

Resource mapping simplifies port assignment problems.

Effective for analyzing SPEC CPU 2017 benchmarks.

Abstract

In a super-scalar architecture, the scheduler dynamically assigns micro-operations ($\mu$OPs) to execution ports. The port mapping of an architecture describes how an instruction decomposes into $\mu$OPs and lists for each $\mu$OP the set of ports it can be mapped to. It is used by compilers and performance debugging tools to characterize the performance throughput of a sequence of instructions repeatedly executed as the core component of a loop. This paper introduces a dual equivalent representation: The resource mapping of an architecture is an abstract model where, to be executed, an instruction must use a set of abstract resources, themselves representing combinations of execution ports. For a given architecture, finding a port mapping is an important but difficult problem. Building a resource mapping is a more tractable problem and provides a simpler and equivalent model. This paper describes Palmed, a tool that automatically builds a resource mapping for pipelined, super-scalar, out-of-order CPU architectures. Palmed does not require hardware performance counters, and relies solely on runtime measurements. We evaluate the pertinence of our dual representation for throughput modeling by extracting a representative set of basic-blocks from the compiled binaries of the SPEC CPU 2017 benchmarks. We compared the throughput predicted by existing machine models to that produced by Palmed, and found comparable accuracy to state-of-the art tools, achieving sub-10 % mean square error rate on this workload on Intel's Skylake microarchitecture.