The annual Super Computing conference (SC) brings together the finest minds in the high performance computing (HPC) industry to showcase the latest technologies and research. While academics and engineers are presenting cutting-edge technology, a unique competition – the Student Cluster Competition - pits students against each other to build a cost effective and energy efficient HPC cluster.

 

At last month's SC'13 conference in Denver, a team from Bentley University and Northeastern University took the top spot in the Student Cluster Competition under the new Commodity Cluster track with a cluster powered by AMD's A-Series Accelerated Processing Units (APU). The team beat others from Slippery Rock University, Skyline High School and Arizona State  University .

 

The team was comprised of five students, with Conner Charlebois, Nicholas Hentschel and Dmitry Veber from Bentley alongside Neel Shah and Tushar Swamyfrom Northeastern. The students were mentored by Bentley Professors Irv Englander and David Yates along with PhD student Yash Ukidave and Professor David Kaeli from Northeastern.

 

 

The Student Cluster Competition comprises of two tracks, with the Standard Track having no budgetary constraints and a Commodity Track placing tough power and monetary limits on competitors. Under the Commodity track, Bentley and Northeastern had to build a HPC cluster within a 15 amps power budget, with components that cost no more than US$2,500.

 

With such tight constraints, the students from Bentley and Northeastern performed experiments to determine the optimal configuration of compute, power utilisation and cost effectiveness and decided that AMD's revolutionary APUs offered them the best chance of winning the prestigious Competition.

 

As the name suggests, the Commodity Track makes takes off-the-shelf hardware to create a HPC cluster. That means anyone can buy the hardware that powered Bentley and Northeastern to glory.

 

The cluster's seven compute nodes were powered by AMD A-Series A10 5800K APU with the head node using an AMD A-Series A10 6800K APU, strapped into ASRock FM2A75M-DGS R2.0 motherboards. Each compute node had 8GB DDR3 clocked at 2,133MHz while the head node had 16GB DDR3 clocked at 1,866MHz. Including the supporting equipment, such as power supply units, hard drives, network switches and cables, the price for an eight node HPC cluster came out to less than US$2,500.

 

The Student Cluster Competition ran for 48 hours, with four industrial-strength benchmarks being used to evaluate the clusters. Prior to the competition the team knew only three of the four applications in-advance, with the final application being disclosed on event day, making the challenge of optimising their cluster even harder.

 

The three applications that were known prior to the competition were: weather research and forecasting, nanoelectronics modelling tools, and GraphLab. As the teams set up the eight-node APU HPC cluster, they were told the final workload was “Flying Snakes,” a computational fluid dynamics simulation. All of these applications provide a good sample of the workloads that are run on multi-million dollar supercomputer clusters.

 

Not only did the Bentley and Northeastern cluster win the Commodity Track, but its score over the four workloads was higher than three teams in the Standard Track. This was an outstanding accomplishment as teams on the Standard Track not only had access to 30 amps of power but also unlimited budget.

 

Yates said, “The Bentley University and Northeastern University students picked the AMD A10 family of APUs for its performance per-dollar. AMD’s system on-a-chip design also gave the team a symmetrical cluster design within this chip family, with x86 double precision floating point performance being most important over the course of the 48-hour  competition .”

 

The floating point performance Yates was referring to comes from a combination of the AMD A10's multi-core x86 architecture and the AMD Radeon GPU that supports the OpenCL™ programming language.

 

Kaeli and his students have long seen the compute potential in APUs. He said, “We have a team here at Northeastern that has been working with APUs and OpenCL for a number of years, and together with the Bentley members of the team, we were able to be very competitive and strategic during the  competition  .”

 

The exploits of Bentley and Northeastern highlight the fundamental compute power that each AMD A-Series APU has by combining a powerful multi-core x86 CPU with a programmable AMD Radeon GPU that supports the industry standard OpenCL programming language. The ability for the team to build an eight node cluster, given the power and budget limitations set by the Student Cluster Competition, shows the incredible performance per-watt per-dollar that an AMD A-Series APU is capable of delivering to desktops, laptops, tablets and even supercomputers.

 

By making the smart choice, Bentley and Northeastern not only won the Student Cluster Competition in style, but showed that AMD's A-Series APUs can mix it in the competitive world of HPC.

 

bentley_neu_team.png

 

Team members

Conner Charlebois, Computer Information Systems undergraduate (class of 2014), Bentley University

Nicholas Hentschel, Computer Information Systems undergraduate (class of 2014), Bentley University

Neel Shah - ECE undergraduate (class of 2015), Northeastern University

Tushar Swamy - ECE undergraduate (class of 2015), Northeastern University

Dmitry Veber, Actuarial Science undergraduate (class of 2014), Bentley University

 

Team mentors

Irv Englander, Professor Emeritus, Computer Information Systems, Bentley University

David Kaeli - ECE Professor, Northeastern University

Yash Ukidave - ECE PhD student, Northeastern University

David Yates, Associate Professor, Computer Information Systems, Bentley University

 

Lawrence Latif is a blogger and technical communications representative at AMD. His postings are his own opinions and may not represent AMD’s positions, strategies or opinions. Links to third party sites, and references to third party trademarks, are provided for convenience and illustrative purposes only. Unless explicitly stated, AMD is not responsible for the contents of such links, and no third party endorsement of AMD or any of its products is implied.