Asset Details
Do you wish to reserve the book?
Revisiting the performance optimization of QR factorization on Intel KNL and SKL multiprocessors
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Revisiting the performance optimization of QR factorization on Intel KNL and SKL multiprocessors
Do you wish to request the book?
Revisiting the performance optimization of QR factorization on Intel KNL and SKL multiprocessors
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Revisiting the performance optimization of QR factorization on Intel KNL and SKL multiprocessors
2024
Overview
This study focused on the optimization of double-precision general matrix–matrix multiplication (DGEMM) routine to improve the QR factorization performance. By replacing the MKL DGEMM with our previously developed blocked matrix–matrix multiplication routine, we found that the QR factorization performance was suboptimal due to a bottleneck in the
A
T
·
B
matrix–panel multiplication operation. We present an investigation of the limitations of our matrix–matrix multiplication routine. It was found that the performance of the matrix multiplication routine depends on the shape and size of the matrices. Therefore, we recommend different kernels tailored to matrix shapes involved in QR factorization and developed a new routine for the
A
T
·
B
matrix–panel multiplication operation. We demonstrated the performance of the proposed kernels on the ScaLAPACK QR factorization routine by comparing them with the MKL, OPENBLAS, and BLIS libraries. Our proposed optimization demonstrates significant performance improvements in the multinode cluster environments of the Intel Xeon Phi Processor 7250 codenamed Knights Landing (KNL) and Intel Xeon Gold 6148 Scalable Skylake Processor (SKL).
Publisher
Springer US,Springer Nature B.V
