98¢/Mflops/s ultra-large-scale neural-network training on a pIII cluster
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
High-cost CFD on a low-cost cluster
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
The Scc Compiler: SWARing at MMX 3DNow!
LCPC '99 Proceedings of the 12th International Workshop on Languages and Compilers for Parallel Computing
A Parallel Processing Support Library Based on Synchronized Aggregate Communication
LCPC '95 Proceedings of the 8th International Workshop on Languages and Compilers for Parallel Computing
KLAT2's flat neighborhood network
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
Switch Design to Enable Predictive Multiplexed Switching in Multiprocessor Networks
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
A compiler-based communication analysis approach for multiprocessor systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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A Flat Neighborhood Network (FNN) is a new interconnection network architecture that can provide very low latency and high bisection bandwidth at a minimal cost for large clusters. However, unlike more traditional designs, FNNs generally are not symmetric. Thus, although an FNN by definition offers a certain base level of performance for random communication patterns, both the network design and communication (routing) schedules can be optimized to make specific communication patterns achieve significantly more than the basic performance. The primary mechanism for design of both the network and communication schedules is a set of genetic search algorithms (GAs) that derive good designs from specifications of particular communication patterns. This paper centers on the use of these GAs to compile the network wiring pattern, basic routing tables, and code for specific communication patterns that will use an optimized schedule rather than simply applying the basic routing.