The Sparse direct methods via Run-time Scheduling and Execution of Kernels with Auto-tunable and Frequency-scaling Features for Energy-aware computing on heterogeneous architectures (SparseKafe) project will create fast and efficient sparse direct methods for platforms with multi-core processors with one or more accelerators (e.g., GPUs or Xeon Phi coprocessors). SparseKaffe spans the platform pyramid, from desktop machines to extreme scale systems consisting of multiple heterogeneous nodes connected through a high-speed network, with the goal of achieving orders of magnitude gains in computational performance, while also paying careful attention to the energy requirements.
The SparseKaffe project is a collaboration between ICL/UTK, the University of Florida, and Texas A&M University. ICL’s work on the project will concentrate on dynamic runtime scheduling using the dataflow model, which will leverage, and be a natural extension of, ICL’s work on runtimes as part of the MAGMA, PLASMA, and PaRSEC projects. The autotuning of the algorithm-specific computational kernels will apply the principles behind ICL’s BEAST project.