Future Proof Data Parallel Algorithms and Software on Intel Multicore Architecture

TL;DR: This paper describes how Ct is designed for minimal effort by the developer, while providing forward scaling on multi-core IA, and describes how a sampling of key application spaces can be easily written using Ct to achieve high performance.

Abstract: Developers face new challenges with multi-core software development. The first of these challenges is a significant productivity burden particular to parallel programming. A big contributor to this burden is the relative difficulty of tracking down data races, which manifest nondeterministically. The second challenge is parallelizing applications so that they effectively scale with new core counts and the inevitable enhancement and evolution of the instruction set. This is a new and subtle change to the benefit of backwards compatibility inherent in Intel Architecture (IA): performance may not scale forward with new micro-architectures and, in some cases, may regress. We assert that forward-scaling is an essential requirement for new programming models, tools, and methodologies intended for multi-core software development. We are implementing a programming model called the Ct API that leverages the strengths of data parallel programming to help address these challenges of multicore software development. In this paper we describe how Ct is designed for minimal effort by the developer, while providing forward scaling on multi-core IA. We describe how Ct’s design and implementation evolved from the initial prototype, based on co-traveler feedback, and we provide examples of how Ct can be used. We demonstrate how a sampling of key application spaces can be easily written using Ct to achieve high performance. Finally, we discuss how these ideas can be transitioned into mainstream software development tools.