Model-driven Level 3 BLAS Performance Optimization on Loongson 3A Processor

By Xianyi Zhang, Qian Wang, Yunquan Zhang. 2012

184 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Matthew Brett
2. K. Jarrod Millman
3. Yunquan Zhang
4. Qian Wang
5. Xianyi Zhang
6. Qing Yi

In Hardware and Architecture

1. Yunquan Zhang
2. Qian Wang
3. Xianyi Zhang
4. Qing Yi
5. Jonathan J. Helmus
6. C. Jaroniec

In Optimization & Training

1. Stephan Hoyer

Citations in the corpus

• Array programming with NumPy (2020, methodology): “3). backends such as OpenBLAS [19, 20] or Intel MKL optimized for the CPUs at hand.”
• MnnFast: A Fast and Scalable System Architecture for Memory-Augmented Neural Networks (2019, methodology): “We run our implementation on Ubuntu 16.04 LTS and use OpenBLAS [75, 82] for BLAS operations.”
• AUGEM: Automatically generate high performance Dense Linear Algebra kernels on x86 CPUs (2013, methodology): “Further, the GEMM kernel generated by our framework for the Intel Sandy Bridge CPU has been adopted as a part of our open-source BLAS library OpenBLAS [21].”