Subject: General Tech | November 30, 2017 - 12:48 AM | Tim Verry
Tagged: HPC, supercomputer, Raspberry Pi 3, cluster, research, LANL
The Raspberry Pi has been used to build cheap servers and small clusters before, but BitScope is taking the idea to the extreme with a professional enterprise solution. On display at SC17, the BitScope Raspberry Pi Cluster Module is a 6U rackable drawer that holds 144 Raspberry Pi 3 single board computers along with all of the power, networking, and air cooling needed to keep things running smoothly.
Each cluster module holds two and a half BitScope Blades with each BitScope Blade holding up to 60 Raspberry Pi PCs (or other SBCs like the ODROID C2). Enthusiasts can already purchase their own Quattro Pi boards as well as the cluster plate to assemble their own small clusters though the 6U Cluster Module drawer doesn’t appear to be for sale yet (heh). Specifically each Cluster Module has room for 144 active nodes, six spare nodes, and one cluster manager node.
For reference, the Raspberry Pi 3 features the Broadcom BCM2837 SoC with 4 ARM Cortex A53 cores at 1.2 GHz and a VideoCore IV GPU that is paired with 1 GB of LPDDR2 memory at 900 MHz, 100 Mbps Ethernet, 802.11n Wi-Fi and Bluetooth. The ODROID C2 has 4 Amlogic cores at 1.5 GHz, a Mali 450 GPU, 2 GB of DDR3 SDRAM, and Gigabit Ethernet. Interestingly, BitScope claims the Cluster Module uses a 10 Gigabit Ethernet SFP+ backbone which will help when communicating between Cluster Modules but speeds between individual nodes will be limited by at best one gigabit speeds (less in real world, and in the case of the Pi it is much less than the 100 Mbps port rating due to how it is wired to the SoC).
BitScope is currently building a platform for Los Alamos National Laboratory that will feature five Cluster Modules for a whopping 2,880 64-bit ARM cores, 720GB of RAM, and a 10GbE SFP+ fabric backbone. Fully expanded, a 42U server cabinet holds 7 modules (1008 active nodes / 4,032 active cores) and would consume up to 6KW of power. LANL expects their 5 module setup to use around 3000 W on average though.
What is the New Mexico Consortium and LANL planning to do with all these cores? Well, playing Crysis would prove tough even if they could SLI all those GPUs so instead they plan to use the Raspberry Pi-powered system to model much larger and prohibitively expensive supercomputers for R&D and software development. Building out a relatively low cost and low power system enables it to be powered on and accessed by more people including students, researchers, and programmers where they can learn and design software that runs as efficiently as possible on massive multiple core and multiple node systems. Getting software to scale out to hundreds and thousands of different nodes is tricky, especially if you want all the nodes working on the same problem(s) at once. Keeping each node fed with data, communicating amongst themselves, and returning accurate results while keeping latency low and utilization high is a huge undertaking. LANL is hoping that the Raspberry Pi based system will be the perfect testing ground for software and techniques they can then use on the big gun supercomputers like Trinity, Titan, Summit (ORNL, slated for 2018), and other smaller HPC clusters.
It is cool to see how far the Raspberry Pi has come and while I wish the GPU was more open so that the researchers could more easily work with heterogenous HPC coding rather than just working with the thousands of ARM cores, it is still impressive to see what is essentially a small supercomputer with a 1008 node cluster for under $25,000!
I am interested to see how the researchers at Los Alamos put it to work and the eventual improvements to HPC and supercomputing software that come from this budget cluster project!
- Intel Hopes For Exaflop Capable Supercomputers Within 10 Years
- The Next Most Powerful Supercomputer in the U.S. Is Almost Complete
- NVIDIA Launches Tesla K20X Accelerator Card, Powers Titan Supercomputer
- GTC 2013: Pedraforca Is A Power Efficient ARM + GPU Cluster For Homogeneous (GPU) Workloads