7 citations · 17 across the 7 of their papers we have counts for
8 papers
How to grade the accuracy of the BLAS
James Demmel, Greg Henry, Igor Kozachenko +4
Motivated by accelerating machine learning (ML), many computer vendors and chip manufacturers are building accelerators for matrix multiplication, which save time and energy by ope…
Exceeding the Numerical and Performance Characteristics of IEEE-754 SGEMM with BFloat16 Tensor Cores on GPUs for Scientific Computing
Harun Bayraktar, Cole Brower, John Gunnels +9
Largely due to their increased native capacity for numerical intensity and power efficiency, reduced-precision floating-point computing resources, primarily used in artificial inte…
Cascading GEMM: High Precision from Low Precision
Devangi N. Parikh, Robert A. van de Geijn, Greg M. Henry
This paper lays out insights and opportunities for implementing higher-precision matrix-matrix multiplication (GEMM) from (in terms of) lower-precision high-performance GEMM. The d…
Proposed Consistent Exception Handling for the BLAS and LAPACK
James Demmel, Jack Dongarra, Mark Gates +7
Numerical exceptions, which may be caused by overflow, operations like division by 0 or sqrt(-1), or convergence failures, are unavoidable in many cases, in particular when softwar…
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
Leveraging the bfloat16 Artificial Intelligence Datatype For Higher-Precision Computations
Greg Henry, Ping Tak Peter Tang, Alexander Heinecke
In recent years fused-multiply-add (FMA) units with lower-precision multiplications and higher-precision accumulation have proven useful in machine learning/artificial intelligence…