Abstract: Hadoop and Spark are widely used distributed processing frameworks for large-scale data processing in an efficient and fault-tolerant manner on private or public clouds. These big-data ...

===== Benchmarks for 3×3 Float64 matrices ===== Matrix multiplication -> 5.9x speedup Matrix multiplication (mutating) -> 1.8x speedup Matrix addition -> 33.1x ...

Abstract: One popular application for big data is matrix multiplication, which has been solved using many approaches. Recently, researchers have applied MapReduce as a new approach to solve this ...

Matrix-vector multiplications form the core of a plethora of scientific computing and machine learning applications that include solving partial differential equations, forward and back propagation in ...

Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data ...

Because the list based, functional toolbox of Raku is not enough to calculate matrices comfortably, there is a need for a dedicated data type. The aim is to provide a full featured set of structural ...