Data-Parallel Computing

ABSTRACT

Users always care about performance.

Although often it's just a matter of making sure the software is doing only what it should, there are many cases where it is vital to get down to the metal and leverage the fundamental characteristics of the processor.

REFERENCES

• 1: Naga Govindaraju , Jim Gray , Ritesh Kumar , Dinesh Manocha, GPUTeraSort: high performance graphics co-processor sorting for large database management, Proceedings of the 2006 ACM SIGMOD international conference on Management of data, June 27-29, 2006, Chicago, IL, USA [doi>10.1145/1142473.1142511]
• 2: Jens Krüger , Rüdiger Westermann, Linear algebra operators for GPU implementation of numerical algorithms, ACM Transactions on Graphics (TOG), v.22 n.3, July 2003
• 3: Blythe, D. 2008. The Rise of the GPU. Proceedings of the IEEE 96(5).
• 4: Shubhabrata, S., Lefohn, A.E., Owens, J.D. 2006. A work-efficient step-efficient prefix sum algorithm. Proceedings of the Workshop on Edge Computing Using New Commodity Architectures: D-26-27.
• 5: Aaron E. Lefohn , Shubhabrata Sengupta , Joe Kniss , Robert Strzodka , John D. Owens, Glift: Generic, efficient, random-access GPU data structures, ACM Transactions on Graphics (TOG), v.25 n.1, p.60-99, January 2006 [doi>10.1145/1122501.1122505]
• 6: See reference 1.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.1 Hardware architecture
          Subjects: Parallel processing

Additional Classification:
  D. Software
  D.1 PROGRAMMING TECHNIQUES
      D.1.3 Concurrent Programming
          Subjects: Parallel programming

  F. Theory of Computation
  F.1 COMPUTATION BY ABSTRACT DEVICES
      F.1.2 Modes of Computation
          Subjects: Parallelism and concurrency

  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.1 Hardware architecture
          Subjects: Graphics processors

General Terms:
Algorithms, Design, Theory