This paper presents and validates performance models for a varietyvof high-performance collective communication algorithms for systems with Cell processors. The systems modeled include a single Cell processor, two Cell chips on a Cell Blade, and a cluster of Cell Blades. The models extend PLogP, the well-known point-topoint performance model, by accounting for the unique hardware characteristics of the Cell (e.g., heterogeneous interconnects and DMA engines) and by applying the model to collective communication. This paper also presents a micro-benchmark suite to accurately measure the extended PLogP parameters on the Cell Blade and then uses these parameters to model different algorithms for the barrier, broadcast, reduce, all-reduce, and all-gather collective operations. Out of 425 total performance predictions, 398 of them see less than 10% error compared to the actual execution time and all of them see less than 15%.

This paper presents high-performance collective communication algorithms and implementations that exploit the unique architectural features of the Cell heterogeneous multicore processor. This paper specifically describes novel algorithms for the barrier, broadcast, reduce, all-reduce, and all-gather collective operations, and shows the efficiency of these by comparing them to the previous

Aspen is a programming language that relies on high-level messaging to support communication among different program tasks executing in parallel. Unlike MPI, the computational logic of Aspen tasks is specified and developed independently of the global communication structure of the program. A root module specifies the communication structure of the program. The semantics and generality of these specifications enable novel forms of collective communication, including asynchronous and concurrent collective operations and reduction type operations with subsets of the participants being receivers of the reduced data, and with receivers that do not provide data to the reduction. This paper describes efficient implementations of these and other collective communication operations in Aspen. We demonstrate the ease-of-use of these features using several code examples and quantify their performance impact through both microbenchmarks and a quantum chemistry code used in rubber chemistry. Aspen's performance is competitive with, or slightly better than, the performance of MPI implementations for both the chemistry application and the microbenchmarks.