This paper presents Tofu, a system that partitions very large DNN models across multiple
GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow …

In recent years the computing landscape has seen an increasing shift towards specialized
accelerators. Field programmable gate arrays (FPGAs) are particularly promising for the …

NumPy is a popular Python library used for performing array-based numerical computations.
The canonical implementation of NumPy used by most programmers runs on a single CPU …

Task partitioning of a graph-parallel system is traditionally considered equivalent to the
graph partition problem. Such equivalence exists because the properties associated with …

High performance in modern computing platforms requires programs to be parallel,
distributed, and run on heterogeneous hardware. However programming such architectures …

Machine learning (ML) computations are often expressed using vectors, matrices, or higher-
dimensional tensors. Such data structures can have many different implementations …

Matrix multiplication is a dominant but very time-consuming operation in many big data
analytic applications. Thus its performance optimization is an important and fundamental …

Deep learning systems have become vital tools across many fields, but the increasing model
sizes mean that training must be accelerated to maintain such systems' utility. Current …

To cope with the rapid growth in available data, the efficiency of data analysis and machine
learning libraries has recently received increased attention. Although great advancements …

The performance of in-memory based data analytic frameworks such as Spark is
significantly affected by how data is partitioned. This is because the partitioning effectively …