Author Archives: John Murphy

John Murphy

About John Murphy

My background in HPC includes building two clusters at the University of Massachusetts, along with doing computational research in quantum chemistry on the facilities of the Massachusetts Green High Performance Computing Center. My personal interests and academic background encompass a range of topics across science and engineering. In recent work, I used the GAMESS quantum chemistry package in order to study theoretical highly strained hydrocarbon structures, derived from prismane building blocks. I also recently authored a small software application in Python for generating amorphous cellulose within a periodic space. This application was used for generating structures for further study in NAMD and LAMMPS. Prior to doing research in Quantum and Materials Chemistry, I worked on problems related to protein folding and docking. It is very exciting, especially, to be involved with applications of GPU computing to these, as well as to other scientific questions. For several years, while not doing research, I was a consulting software engineer and built a variety of internet and desktop software applications. As an HPC Sales Specialist at Microway, I greatly look forward to advising Microway's clients in order to provide them with well-configured, optimal HPC solutions.

One-shot Learning Methods Applied to Drug Discovery with DeepChem

Posted on July 26, 2017 by John Murphy

Experimental data sets for drug discovery are sometimes limited in size, due to the difficulty of gathering this type of data. Drug discovery data sets are expensive to obtain, and some are the result of clinical trials, which might not … Continue reading →

DeepChem – a Deep Learning Framework for Drug Discovery

Posted on April 28, 2017 by John Murphy

A powerful new open source deep learning framework for drug discovery is now available for public download on github. This new framework, called DeepChem, is python-based, and offers a feature-rich set of functionality for applying deep learning to problems in … Continue reading →

Deep Learning Benchmarks of NVIDIA Tesla P100 PCIe, Tesla K80, and Tesla M40 GPUs

Posted on January 27, 2017 by John Murphy

Sources of CPU benchmarks, used for estimating performance on similar workloads, have been available throughout the course of CPU development. For example, the Standard Performance Evaluation Corporation has compiled a large set of applications benchmarks, running on a variety of … Continue reading →

More Tips on OpenACC Acceleration

Posted on July 25, 2016 by John Murphy

One blog post may not be enough to present all tips for performance acceleration using OpenACC. So here, more tips on OpenACC acceleration are provided, complementing our previous blog post on accelerating code with OpenACC. Further tips discussed here are: … Continue reading →

Deep Learning Applications in Science and Engineering

Posted on June 29, 2016 by John Murphy

Over the past decade, and particularly over the past several years, Deep learning applications have been developed for a wide range of scientific and engineering problems. For example, deep learning methods have recently increased the level of significance of the … Continue reading →

Accelerating Code with OpenACC and the NVIDIA Visual Profiler

Posted on March 14, 2016 by John Murphy

Comprised of a set of compiler directives, OpenACC was created to accelerate code using the many streaming multiprocessors (SM) present on a GPU. Similar to how OpenMP is used for accelerating code on multicore CPUs, OpenACC can accelerate code on … Continue reading →

Deep Learning Frameworks: A Survey of TensorFlow, Torch, Theano, Caffe, Neon, and the IBM Machine Learning Stack

Posted on January 13, 2016 by John Murphy

The art and science of training neural networks from large data sets in order to make predictions or classifications has experienced a major transition over the past several years. Through popular and growing interest from scientists and engineers, this field … Continue reading →

Keras and Theano Deep Learning Frameworks

Posted on October 5, 2015 by John Murphy

Here we will explore how to use the Theano and Keras Python frameworks for designing neural networks in order to accomplish specific classification tasks. In the process, we will see how Keras offers a great amount of leverage and flexibility … Continue reading →

Caffe Deep Learning Tutorial using NVIDIA DIGITS on Tesla K80 & K40 GPUs

Posted on September 17, 2015 by John Murphy

In this Caffe deep learning tutorial, we will show how to use DIGITS in order to train a classifier on a small image set.  Along the way, we’ll see how to adjust certain run-time parameters, such as the learning rate, … Continue reading →