Multi-Core Software

Future-Proof Data Parallel Algorithms and Software on Intel® Multi-Core Architecture

Anwar Ghuloum, Corporate Technology Group, Intel Corporation
Terry Smith, Corporate Technology Group, Intel Corporation
Gansha Wu, Corporate Technology Group, Intel Corporation
Xin Zhou, Corporate Technology Group, Intel Corporation
Jesse Fang, Corporate Technology Group, Intel Corporation
Peng Guo, Corporate Technology Group, Intel Corporation
Byoungro So, Corporate Technology Group, Intel Corporation
Mohan Rajagopalan, Corporate Technology Group, Intel Corporation
Yongjian Chen, Corporate Technology Group, Intel Corporation
Biao Chen, Corporate Technology Group, Intel Corporation

Index words: parallel programming, data parallelism, forward scalability

Citations for this paper. Ghuloum, A.; Smith, T.; Wu, G.; Zhou, X.; Fang, J.; Guo, P.; So, B.; Rajagopalan, M.; Chen, Y.; Chen B. "Future-Proof Data Parallel Algorithms and Software on Intel® Multi-Core Architecture." Intel Technology Journal. http://www.intel.com/technology/itj/2007/
v11i4/7-future-proof/1-abstract.htm (November 2007).

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 non-deterministically. 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 multi-core 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.

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