The potential of the cell processor for scientific computing

By Samuel Williams, John M. Shalf, Leonid Oliker, Shoaib Kamil, Parry Husbands, K. Yelick. 2005

396 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. K. Yelick
2. Samuel Williams
3. J. Demmel
4. John M. Shalf
5. R. Vuduc

In Computer Networks and Communications

1. J. Demmel
2. R. Vuduc
3. K. Yelick
4. Leonid Oliker
5. Samuel Williams
6. John M. Shalf
7. Aydin Buluç
8. Saman P. Amarasinghe

In Hardware and Architecture

1. J. Demmel
2. R. Vuduc
3. K. Yelick
4. Leonid Oliker
5. John M. Shalf
6. Samuel Williams
7. Aydin Buluç
8. Saman P. Amarasinghe

Citations in the corpus

• Optimization of sparse matrix-vector multiplication on emerging multicore platforms (2007, methodology): “For the Cell processor we implemented a SpMV routine similar to [13], which uses only dense cache blocks and virtually no other optimization aside from the mandatory DMAs and compressed 2 byte indices.”
• Ray Tracing on the Cell Processor (2006, background): “The streaming approach works best if the individual tasks can be cascaded and if there is a steady flow of data from one task (i.e., SPE) to the next (as in video or speech processing, or in scientific computations) [ 31 ].”
• High performance combinatorial algorithm design on the Cell Broadband Engine processor (2007, background): “Also, our model does not consider the effect of floating-point precision on the performance of numerical algorithms, which can be quite significant [25].”