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.

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

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

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

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

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

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

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

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