Search Results for author:
Found 8 papers, 6 papers with code
Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch
Linear algebra operations, which are ubiquitous in machine learning, form major performance bottlenecks.
Concurrent Alternating Least Squares for multiple simultaneous Canonical Polyadic Decompositions
We observe that, in practice, experts often have to compute multiple decompositions of the same tensor, each with a small number of components (typically fewer than 20), to ultimately find the best ones to use for the application at hand.
Linnea: Automatic Generation of Efficient Linear Algebra Programs
In order to combine the productivity offered by high-level languages, and the performance of low-level kernels, we are developing Linnea, a code generator for linear algebra problems.
Mathematical Software
Automatic Generation of Efficient Linear Algebra Programs
In order to both achieve the productivity that comes with high-level languages, and make use of the efficiency of low level kernels, we are developing Linnea, a code generator for linear algebra problems.
Mathematical Software
Extended pipeline for content-based feature engineering in music genre recognition
We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification.
Design of a high-performance GEMM-like Tensor-Tensor Multiplication
We present "GEMM-like Tensor-Tensor multiplication" (GETT), a novel approach to tensor contractions that mirrors the design of a high-performance general matrix-matrix multiplication (GEMM).
Mathematical Software Performance G.4; D.3.4; I.1.2; I.1.3
TTC: A high-performance Compiler for Tensor Transpositions
We present TTC, an open-source parallel compiler for multidimensional tensor transpositions.
Mathematical Software Distributed, Parallel, and Cluster Computing Performance
Algorithm 979: Recursive Algorithms for Dense Linear Algebra -- The ReLAPACK Collection
To exploit both memory locality and the full performance potential of highly tuned kernels, dense linear algebra libraries such as LAPACK commonly implement operations as blocked algorithms.
Mathematical Software Performance
