BLIS: A Framework for Rapidly Instantiating BLAS Functionality
By F. V. Zee, R. V. D. Geijn. ACM Transactions on Mathematical Software, 2015
415 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/355e35184d084abc712c5bfcceafc0fdfe78ceef
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
- Jongsoo Park
- Josep Torrellas
- M. Smelyanskiy
- Daya Shanker Khudia
- Jianyu Huang
- Christopher W. Fletcher
- David D. Cox
- Haixin Liu
In Hardware and Architecture
- Jongsoo Park
- Josep Torrellas
- M. Smelyanskiy
- Daya Shanker Khudia
- Jianyu Huang
- Christopher W. Fletcher
- Haixin Liu
- Protonu Basu
Citations in the corpus
- FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference (2021, methodology): “A key ingredient of fbgemm is performant low-precision gemm, which we have implemented using an approach similar to the one taken by other research works [3, 9, 11] targeting FP32 and FP64 data types but not low-precision.”
- Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures (2018, methodology): “Examples of such libraries are OpenBLAS [31], BLIS [39], Intel MKL [33] and AMD ACML [40].”
- Triton: an intermediate language and compiler for tiled neural network computations (2019, background): “Traditional auto-tuners [42, 45] typically rely on hand-written parameterized code templates to achieve good performance on pre-defined workloads.”