Calxeda Takes Aim at Big Data HPC with ARM Server Chip

By Michael Feldman

May 31, 2012

With Dell’s news this week of its renewed plans to bring ARM-based servers to datacenters and Intel’s recent unveiling of new Xeon CPUs aimed at ultra-low-power servers, the “microserver” marketplace is being primed for some commercial offerings. Chip startup Calxeda has been working to bring its own ARM-based SoC technology into the datacenter and, with the help of its OEM partners, the company is positioning the technology for its commercial debut.

The microserver phenomenon is just emerging, but it has all the earmarks of a disruptive market shift. The concept was invented to more closely match hardware capabilities with evolving datacenter workloads and energy usage. A near insatiable demand for Web-based serving and content delivery and a plethora of big data applications, combined with the escalating costs of power and cooling, has forced CPU makers to rethink their priorities. Calxeda, Marvell, Intel, and others recognized these trends forming years ago and started designing ultra-low-power parts aimed at these high-growth application areas.

High performance computing is somewhat on the periphery of this phenomenon. The HPC user’s obsession with performance, especially floating point performance, is rather at odds with these FLOP-challenged chips. And for the initial crop of ARM-based servers, there is the additional limitation of 32-bit computing, which cuts across both HPC and enterprise computing.

Calxeda’s EnergyCore processor, for example, is a quad-core ARM chip of Cortex A9 vintage, the same 32-bit architecture that powers the latest dual-core iPad (sans the PowerVR GPU). And although the Calxeda chip is marginally faster than the iPad chip, its top speed is just 1.4 GHz. With less than half the clock frequency and half the number of cores of a midrange Xeon CPU, the EnergyCore has about 1/10 the overall performance of a Sandy Bridge E5-2600.

The upside, of course, is power usage. While that same 8-core Sandy Bridge part has a 100-watt TDP, the EnergyCore SoC maxes out at less than less than 4 watts. And that includes a high performance on-chip fabric switch, which eliminates the need for a lot of network cabling and energy-sucking switches. The chip also incorporates a management engine that does high-level functions like intelligent node routing and power optimization.

When you add in 4 GB of RAM, a complete Calxeda server is only 5 watts. A four-node, 16-core reference board designed by Calxeda consumes just 20 watts and is 10 inches long.

The catch is that the application has to parallelize rather well. According to the chipmaker, what would have taken 400 servers of a conventional x86 setup now requires 1,600 Calxeda-based servers, albeit with just 1/10 the power requirement, 1/20 the rack footprint, and less than half the up-front costs. That level of savings is attracting a lot of attention from users with cluster apps that can scale reasonably well but don’t require scads of single-threaded performance or raw FLOPS.

That represents a large number of Web and enterprise workloads, but there is also a rather nice subset of HPC applications that can take advantage of this platform. According to Calxeda vice president of marketing Karl Freund, a lot of data-heavy HPC applications are fair game for ARM clusters. Any MapReduce/Hadoop-type application or really any code that is I/O- or memory-bound, rather than compute-bound, is a “great fit” says Freund.

It includes a number of big data-ish apps like financial and risk modeling, seismic codes, and various type of signal processing workloads. Freund also thinks there’s a case to be made here for genomic analysis. In these applications, performance tends to be constrained by the bottleneck at external storage and/or main memory, so you don’t need a fast clock on the CPU; it’s going to be waiting for data regardless of its GHz rating.

In fact, for data-bound codes, the slower the chip the better the performance per watt. That’s the essential design point of these ARM server chips, since they are geared for throughput processing on embarrassingly parallel workloads. And in many cases, you don’t need that much floating point horsepower either.

Even for traditional HPC science simulations, where floating point performance is often critical, the Calxeda solution might be the way to go. Although Freund admits that their CPU is not designed for FP performance, the hardware does include an FPU with both single and double precision FP support, not to mention a NEON SIMD engine with even better single precision performance. But it is by no means a high-end floating point microprocessor in the fashion of a Xeon or an Opteron.

Even in HPC though, that’s not always necessary. In conversations with users at Sandia National Labs, Freund related that only about 5 percent of the aggregate cycles on the labs’ simulation codes were double precision floating point operations. That suggests the Calxeda offering might be able to effectively negotiate a simulation code, slowing down on the floating point curves and making up time on the integer straightaways.

Another consideration is the movement toward heterogenous computing in HPC, where GPUs and to a lesser extent, FPGAs, are being employed as computational accelerators. Where applications can take advantage of such acceleration, a low-power ARM, rather than a big Xeon or Opteron, may be all that’s necessary for a host-side CPU. Freund says at least one customer is toying with the idea of hooking a Calxeda-based server to an FPGA for just such an arrangement.

To date, the company has attracted five OEMs that have designed servers around the EnergyCore SoC. HP and Boston Limited have demonstrated their Calxeda gear in public. HP’s offering, the Redstone Development Platform (4U 288 nodes), is not a commercial product, per se. It’s being distributed to select customers for testing and evaluation only. The Boston Limited platform, known as Viridis (2U 48 nodes), is also in the pre-commercial stage and is likewise being distributed to “interested parties.” And although Dell’s “Copper” microserver is officially powered by Marvell’s ARM server chip, the server maker is also in cahoots with Calxeda on other designs.

The remaining two Calxeda OEMs will remain nameless for the time being. However, according to Freund, three of the five system vendors should begin shipping Calxeda-powered servers in volume by Q4 of this year.

In the meantime, Sandia and MIT have signed up as beta sites for running some HPC codes through Calxeda hardware. A set of HPC libraries and packages have already been ported to the platform, including various flavors of MPI, BLAS, ScaLAPACK, Ganglia (monitoring) and Condor (checkpointing). Language support, including C, Fortran, Perl, Python, and Ruby is there as well.

Let the benchmarking begin.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a ne Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" process Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This