A High-Performance SIMD Floating Point Unit for BlueGene/L: Architecture, Compilation, and Algorithm Design

  • Authors:

  • Leonardo Bachega;Siddhartha Chatterjee;Kenneth A. Dockser;John A. Gunnels;Manish Gupta;Fred G. Gustavson;Christopher A. Lapkowski;Gary K. Liu;Mark P. Mendell;Charles D. Wait;T. J. Chris Ward

  • Affiliations:

  • IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM Corporation, Research Triangle Park, NC;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM Corporation, Markham, ON, Canada;IBM Corporation, Markham, ON, Canada;IBM Corporation, Markham, ON, Canada;IBM Corporation, Rochester, MN;IBM T. J. Watson Research Center, Yorktown Heights, NY

  • Venue:
  • Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques
  • Year:
  • 2004

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Abstract

We describe the design, implementation, and evaluation of a dual-issue SIMD-like extension of the PowerPC 440 floating-point unit (FPU) core. This extended FPU is targeted at both IBM's massively parallel Blue-Gene/L machine as well as more pervasive embedded platforms. It has several novel features, such as a computational crossbar and cross-load/store instructions, which enhance the performance of numerical codes. We further discuss the hardware-software co-design that was essential to fully realize the performance benefits of the FPU when constrained by the memory bandwidth limitations and high penalties for misaligned data access imposed by the memory hierarchy on a BlueGene/L node. We describe several novel compiler and algorithmic techniques to take advantage of this architecture. Using both hand-optimized and compiled code for key linear algebraic kernels, we validate the architectural design choices, evaluate the success of the compiler, and quantify the effectiveness of the novel algorithm design techniques. Preliminary performance data shows that the algorithm-compiler-hardware combination delivers a significant fraction of peak floating-point performance for compute-bound kernels such as matrix multiplication, and delivers a significant fraction of peak memory bandwidth for memory-bound kernels such as daxpy, while being largely insensitive to data alignment.