Conjugate gradient solvers on Intel Xeon Phi and NVIDIA GPUs

Understand

Lattice Quantum Chromodynamics simulations typically spend most of the runtime in inversions of the Fermion Matrix.

• This part is therefore frequently optimized for various HPC architectures.
• Here we compare the performance of the Intel Xeon Phi to current Kepler-based NVIDIA Tesla GPUs running a conjugate gradient solver.
• By exposing more parallelism to the accelerator through inverting multiple vectors at the same time, we obtain a performance greater than 300 GFlop/s on both architectures.