Implementing Software Resiliency in HPX for Extreme Scale Computing

TL;DR

This paper presents the implementation of software resiliency features in the HPX runtime system, enabling applications to handle hardware failures more reliably with minimal overhead, especially for larger tasks.

Contribution

The paper introduces two new resiliency APIs—task replication and task replay—in HPX, along with a verification API, enhancing fault tolerance for extreme scale computing.

Findings

Resiliency APIs incur minor overheads for tasks >200 μs.

Task replay and replication dominate execution time when used.

APIs effectively improve fault tolerance with minimal performance impact.

Abstract

Exceptions and errors occurring within mission critical applications due to hardware failures have a high cost. With the emerging Next Generation Platforms (NGPs), the rate of hardware failures will invariably increase. Therefore, designing our applications to be resilient is a critical concern in order to retain the reliability of results while meeting the constraints on power budgets. In this paper, we implement software resilience in HPX, an Asynchronous Many-Task Runtime system. We implement two resiliency APIs that we expose to the application developers, namely task replication and task replay. Task replication repeats a task n-times and executes them asynchronously. Task replay will reschedule a task up to n-times until a valid output is returned. Furthermore, we introduce an API that allows the application to verify the returned result with a user provided predicate. We test the APIs with both artificial workloads and a dataflow based stencil application. We demonstrate that only minor overheads are incurred when utilizing these resiliency features for work loads where the task size is greater than 200 $\mu$s. We also show that most of the added execution time arises from the replay or replication of the tasks themselves and not by the implementation of the APIs.