Fast and Accurate Simulation of the Cray XMT Multithreaded Supercomputer

Abstract

Irregular applications, such as data mining or graph-based computations, show unpredictable memory/network access patterns and control structures. Massively multithreaded architectures with large processor counts, like the Cray MTA-1, MTA-2, and XMT, appear to address irregular application requirements better than commodity clusters. However, the research on massively multithreaded systems is currently limited by the lack of adequate architectural simulation infrastructures due to issues such as size of the machines, memory footprint, simulation speed, accuracy, and customization. At the same time, Shared Memory MultiProcessors (SMPs) with multicore processors have become an attractive platform to simulate large-scale systems. This paper introduces a cycle-level simulator of the massively multithreaded Cray XMT supercomputer. The simulator runs unmodified XMT applications. We discuss how we tackled the challenges posed by its development, detailing the techniques implemented to obtain high-simulation speed while maintaining a high accuracy. By mapping XMT processors (ThreadStorm with 128 hardware threads) to host computing cores, the simulation speed remains constant as the number of simulated processors increases, up to the number of available host cores. The simulator supports zero-overhead switching among different accuracy levels at runtime and includes a parametric network and memory model that takes into account contention and hot spotting. On a modern 48-core SMP host, the proposed infrastructure simulates a large set of irregular applications 500 to 2,000 times slower than real time when compared to a 128-processor XMT, with an accuracy error under 10 percent. Emulation is only from 25 to 200 times slower than real time. The paper also presents a case study, where the simulation infrastructure is used to identify bottlenecks in the current XMT architecture and to estimate the performance scaling of a possible multicore design with next generation memory and network interconnect.