AMD and Intel have now published a full technical specification for ACE — AI Compute Extensions — the most significant overhaul to x86 AI compute in the architecture's history, co-authored by eight ...

Department of Engineering Technology, Savannah State University, Savannah, GA, USA. Classical algorithms can use loops with arbitrary depth because classical bits persist in physical memory—the state ...

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 ...

To quantify the fitness of each lineage, we developed a multinomial logistic model to fit the proportion of tips and nodes that belong to each lineage through time. We assumed each lineage has a ...

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 ...

Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot ...

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 ...