We review our work done to optimize the staggered conjugate gradient (CG) algorithm in the MILC code for use with the Intel Knights Landing (KNL) architecture. KNL is the second generation Intel Xeon Phi processor. It is capable of massive thread parallelism, data parallelism, and high on-board memory bandwidth and is being adopted in supercomputing centers for scientific research. The CG solver consumes the majority of time in production running, so we have spent most of our effort on it. We compare performance of an MPI+OpenMP baseline version of the MILC code with a version incorporating the QPhiX staggered CG solver, for both one-node and multi-node runs
The High-Performance Conjugate Gradient (HPCG) benchmark complements the LINPACK benchmark in the pe...
Reconfigurable computing can significantly improve the performance and energy efficiency of many app...
We investigate the efficiency of state-of-the-art multicore processors using a multi-threaded task-p...
With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU pro...
The conjugate gradient (CG) algorithm is among the most essential and time consuming parts of lattic...
Recently, we have benchmarked and tuned the MILC code on a number of architectures including Intel I...
Recently, we have benchmarked and tuned the MILC code on a number of architectures including Intel I...
Kaczmarek O, Schmidt C, Steinbrecher P, Wagner M. Conjugate gradient solvers on Intel Xeon Phi and N...
The Knights Landing (KNL) is the codename for the latest generation of Intel processors based on Int...
For large sparse unstructured matrices, the critical parts of the Conjugate Gradient method on the i...
The Roofline Performance Model is a visually intuitive method used to bound the sustained peak float...
Abstract—A new sparse high performance conjugate gradient benchmark (HPCG) has been recently release...
This paper examines four different strategies for implementing the parallel Conjugate Gradient (CG) ...
The performance of conjugate gradient (CG) algorithms for the solution of the system of linear equat...
The article is devoted to the vectorization of calculations for Intel Xeon Phi Knights Landing (KNL)...
The High-Performance Conjugate Gradient (HPCG) benchmark complements the LINPACK benchmark in the pe...
Reconfigurable computing can significantly improve the performance and energy efficiency of many app...
We investigate the efficiency of state-of-the-art multicore processors using a multi-threaded task-p...
With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU pro...
The conjugate gradient (CG) algorithm is among the most essential and time consuming parts of lattic...
Recently, we have benchmarked and tuned the MILC code on a number of architectures including Intel I...
Recently, we have benchmarked and tuned the MILC code on a number of architectures including Intel I...
Kaczmarek O, Schmidt C, Steinbrecher P, Wagner M. Conjugate gradient solvers on Intel Xeon Phi and N...
The Knights Landing (KNL) is the codename for the latest generation of Intel processors based on Int...
For large sparse unstructured matrices, the critical parts of the Conjugate Gradient method on the i...
The Roofline Performance Model is a visually intuitive method used to bound the sustained peak float...
Abstract—A new sparse high performance conjugate gradient benchmark (HPCG) has been recently release...
This paper examines four different strategies for implementing the parallel Conjugate Gradient (CG) ...
The performance of conjugate gradient (CG) algorithms for the solution of the system of linear equat...
The article is devoted to the vectorization of calculations for Intel Xeon Phi Knights Landing (KNL)...
The High-Performance Conjugate Gradient (HPCG) benchmark complements the LINPACK benchmark in the pe...
Reconfigurable computing can significantly improve the performance and energy efficiency of many app...
We investigate the efficiency of state-of-the-art multicore processors using a multi-threaded task-p...