Julia Implementation of the Dynamic Distributed Dimensional Data Model

Abstract

Julia is a new language for writing data analysis programs that are easy to implement and run at high performance. Similarly, the Dynamic Distributed Dimensional Data Model (D4M) aims to clarify data analysis operations while retaining strong performance. D4M accomplishes these goals through a composable, unified data model on associative arrays. In this work, we present an implementation of D4M in Julia and describe how it enables and facilitates data analysis. Several experiments showcase scalable performance in our new Julia version as compared to the original Matlab implementation.

7 pages, 16 figures, IEEE HPEC 2016

References in corpus (6)

• Community detection in graphs
• Computing in Operations Research using Julia
• Julia: A Fresh Approach to Numerical Computing
• D4M 2.0 Schema: A General Purpose High Performance Schema for the Accumulo Database
• Big Data Dimensional Analysis
• Genetic Sequence Matching Using D4M Big Data Approaches

Cited by in corpus (7)

• Polystore Mathematics of Relational Algebra
• Deployment of Real-Time Network Traffic Analysis using GraphBLAS Hypersparse Matrices and D4M Associative Arrays
• Mathematics of Digital Hyperspace
• TabulaROSA: Tabular Operating System Architecture for Massively Parallel Heterogeneous Compute Engines
• D4M 3.0: Extended Database and Language Capabilities
• Database Operations in D4M.jl
• Python Implementation of the Dynamic Distributed Dimensional Data Model