Exploitation of Massively Parallel Architectures for Drug Discovery

High Performance Networks for Massively Parallel Architectures

The past few years have seen a rise in popularity of massively parallel architectures that use fat-trees as their interconnection networks. In this paper we study the communication performance of a parametric family of fat-trees, the k-ary n-trees, built with constant arity switches interconnected in a regular topology. Through simulation on a 4-ary 4-tree with 256 nodes, we analyze some varian...

Compiling FORTRAN for Massively Parallel Architectures

Compiling for massively parallel architectures: a perspective

The problem of automatically generating programs for massively parallel computers is a very complicated one, mainly because there are many architectures, each of them seeming to pose its own particular compilation problem. The purpose of this paper is to propose a framework in which to discuss the compilation process, and to show that the features which aaect it are few and generate a small num...

Performance Analysis of Massively Parallel Architectures

Cube based networks have received much attention over the past decade since they offer a rich interconnected structure with a number of desirable properties such as low diameter, high bisection width, lesser complexity and Cost. Among them the hypercube architecture is widely used network for parallel computer system due to its low diameter. The major drawback of hypercube based architectures i...

Massively Multitask Networks for Drug Discovery

Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framew...