Co-founder and co-editor Nicole Hemsoth brings insight from the world of high performance computing hardware and software as well as data-intensive systems and frameworks. Hemsoth is former Editor in Chief of long-standing supercomputing magazine, HPCwire. She was founding editor and conceptual creator of the data-intensive computing magazine Datanami, as well as the conceptual creator and founding Senior Editor for the large-scale infrastructure focused EnterpriseTech.
August 15, 2017 Nicole Hemsoth
It would be surprising to find a Hadoop shop that builds a cluster based on the high-end 68+ core Intel Knights Landing processors—not just because of the sheer horsepower (read as “expense”) for workloads that are more data-intensive versus compute-heavy, but also because of a mismatch between software and file system elements.
Despite these roadblocks, work has been underway at Intel’s behest to prime Knights Landing clusters for beefier Hadoop/MapReduce and machine learning jobs at one of its parallel computing centers at Indiana University.
According to Judy Qiu, associate professor of intelligent systems engineering in IU’s computing division, it is …Read more
August 14, 2017 Nicole Hemsoth
If anything has become clear over the last several years of watching infrastructure and application trends among SaaS-businesses, it is that nothing is as simple as it seems. Even relatively straightforward services, like transactional email processing, have some hidden layers of complexity, which tends to equal cost.
For most businesses providing web-based services, the solution for complexity was found by offloading infrastructure concerns to the public cloud. This provided geographic availability, pricing flexibility, and development agility, but not all web companies went the cloud route out of the gate. Consider SendGrid, which pushes out over 30 billion emails per month. …Read more
August 14, 2017 Nicole Hemsoth
The software ecosystem in high performance computing is set to be more complex with the leaps in capability coming with next generation exascale systems. Among several challenges is making sure that applications retain their performance as they scale to higher core counts and accelerator-rich systems.
Software development and performance profiling company, Allinea, which has been around for almost two decades in HPC, was recently acquired by ARM to add to the company’s software ecosystem story. We talked with one of the early employees of Allinea, VP of Product Development, Mark O’Connor about what has come before—and what the software performance …Read more
August 11, 2017 Nicole Hemsoth
While it is not likely we will see large supercomputers on the International Space Station (ISS) anytime soon, HPE is getting a head start on providing more advanced on-board computing capabilities via a pair of its aptly-named “Apollo” water-cooled servers in orbit.
The two-socket machines, connected with Infiniband will put Broadwell computing capabilities on the ISS, mostly running benchmarks, including High Performance Linpack (HPL), the metric that determines the Top 500 supercomputer rankings. These tests, in addition to the more data movement-centric HPCG benchmark and NASA’s own NAS parallel benchmark will determine what performance changes, if any, are to be …Read more
August 10, 2017 Ken Strandberg
In the following interview, Dr. Matt Leininger, Deputy for Advanced Technology Projects at Lawrence Livermore National Laboratory (LLNL), one of the National Nuclear Security Administration’s (NNSA) Tri Labs describes how scientists at the Tri Labs—LLNL, Los Alamos National Laboratory (LANL), and Sandia National Laboratories (SNL)—carry out the work of certifying America’s nuclear stockpile through computational science and focused above-ground experiments.
We spoke with Dr. Leininger about some of the workflow that Tri Labs scientists follow, how the Commodity Technology Systems clusters are used in their research, and how machine learning is helping them.
The overall goal is to demonstrate a …Read more
August 8, 2017 Nicole Hemsoth
Google has been at the bleeding edge of AI hardware development with the arrival of its TPU and other system-scale modifications to make large-scale neural network processing efficient and fast.
But just as these developments come to fruition, advances in trimmed-down deep learning could move many more machine learning training and inference operations out of the datacenter and into your palm.
Although it might be natural to think the reason that neural networks cannot be processed on devices like smartphones is because of limited CPU power, the real challenge lies in the vastness of the model sizes and hardware memory …Read more
August 8, 2017 Nicole Hemsoth
Novel architectures are born out of necessity and for some applications, including molecular dynamics, there have been endless attempts to push parallel performance.
In this area, there are already numerous approaches to acceleration. At the highest end is the custom ASIC-driven Anton machine from D.E. Shaw, which is the fastest system, but certainly not the cheapest. On the more accessible accelerators side are Tesla GPUs for accelerating highly parallel parts of the workload—and increasingly, FPGAs are being considered for boosting the performance of major molecular dynamics applications, most notably GROMACS as well as general purpose, high-end CPUs (Knights Landing …Read more
August 7, 2017 Nicole Hemsoth
Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. This can be addressed through various model optimizations, but as some argue, the efficiency and utilization gaps can also be addressed with a tailored compiler.
Eugenio Culurciello, an electrical engineer at Purdue University, argues that getting full computational efficiency out of custom deep learning accelerators is difficult. This prompted his team at Purdue to build an FPGA based accelerator that could be agnostic to CNN …Read more
August 2, 2017 Nicole Hemsoth
Ziyang Xu from Peking University in Beijing sees several similarities between the human brain and Von Neumann computing devices.
While he believes there is value in neuromorphic, or brain-inspired, chips, with the right operating system, standard processors can mimic some of the efficiencies of the brain and achieve similar performance for certain tasks.
In short, even though our brains do not have the same high-speed, high-frequency capacity of modern chips, the way information is routed and addressed is the key. At the core of this efficiency is a concept similar to a policy engine governing information compression, storage, and retrieval. …Read more