Startup Uses Data Compression to Speed Applications

By Michael Feldman

January 25, 2012

Silicon Valley-based Samplify Systems has launched an application acceleration technology designed to speed up codes that sling a lot of numerical data. But rather than throwing bigger, faster hardware at the problem, the company aims to make programs speedier by optimizing the data flow between the compute cores and the outside world.

Samplify, whose roots are in signal compression has extended the technology to address all numerically-intensive applications. For HPC users, Samplify’s heart is certainly in the right place. In high performance computing, application acceleration is the thirst that can never to be quenched.

The current performance issue in the industry is related to multicore designs. Processors are getting more powerful at a Moore’s Law clip thanks to a proliferation of cores, while bandwidth of external subsystems like memory and I/O is increasing much more slowly (and in discrete steps). That imbalance is the principle reason that HPC applications typically utilize just a small fraction of their hardware hosts.

For example, a science code called ECCO (Estimating Circulation and Climate of Ocean), one of the workhouse applications on NASA’s Pleiades supercomputer, uses only about 1.4 percent of the 1.3 petaflop peak performance of the machine. That’s due mainly to the fact that the CPUs are spending the majority of their time idly waiting for data to arrive. Unfortunately, that scenario is not much of an outlier. According to a research study at NASA, most of the applications on Pleiades have sustained performance in the 3 to 8 percent range.

Such stark inefficiencies are what drove Samplify to come up with its application acceleration offering, known as APAX. In a nutshell, APAX compresses numerical data (both integer and floating point) that flows through a system, thereby increasing data throughput. And it does so in a manner that is transparent to the application.

The technology is being offered in both hardware and software forms and can be deployed in a variety of ways. Specifically, the compression technology can be inserted at all the usual choke points in a computer system — memory, I/O, networks and storage — in order to attack application bottlenecks at their source.

According to Al Wegener, Samplify founder and CTO, most HPC users are not aware that there are a significant number of bits that can be squeezed out of their codes. In today’s supercomputing culture, the traditional solution to data bandwidth constraints has been to over-provision the hardware. “All of the HPC customers we talked to have never even thought of using compression for their applications,” Wegener told HPCwire.

The APAX technology supports both lossless and lossy compression schemes. Under APAX, the lossless scheme usually attains at least a 2:1 compression, effectively doubling bandwidth at the intended choke point. With lossy schemes, which are under user control, APAX compression can easily hit 4:1 and go as high as 8:1.

Here though, the user has to be somewhat careful, since lossy schemes rely on trimming off some of the numerical precision. In general, application programmers tend to be a bit lazy with precision, preferring to use generic data types in their codes and gravitating towards double precision floating point. But casting 12-bit integer inputs into 64-bit floating point values for the sake of convenience doesn’t magically increase the accuracy of the results and ends up wasting a lot of bits. In working with trial customers, Samplify has found that most applications can tolerate lossy compression in the 4:1 to 6:1 range before the results start to diverge.

From a performance per watt perspective, APAX hardware is probably the most efficient way to go. For example, if a chipmaker wanted to insert compression into its memory controller design, it would simply license the APAX IP block (a couple of hundred logic gates) from Samplify.

Once in the controller, the compression logic, along with compatible drivers, would squeeze the bits sent from the compute cores such that all numerical data would be stored in DRAM in a compressed format. When reading from memory, the same logic would decompress the data before passing it back to the number crunching silicon. Assuming 2:1 compression, memory bandwidth for all numerical data traffic would be doubled. Conveniently, it would also double the effective memory storage.

In an HPC environment, that can add up quickly. Wegener offers the case of NVIDIA’s Tesla GPU devices. Using 2:1 compression, the GPU card’s 6 GB of GDDR5 memory turn into 12 GB of effective storage. Likewise, the 150 GB/sec of bandwidth becomes 300 GB/sec. “I think that would be a big deal,” says Wegener.

Other likely targets for the compression technology would be network adapters, such as InfiniBand or Ethernet NICs, storage controllers, and Southbridge chips. Along with modified drivers, the compression-spiked ASICs would be able to turbo-charge data performance across a system, cluster or even a whole datacenter.

For HPC applications on existing hardware, the most straightforward method is to insert the APAX software into existing applications or wrap it around MPI libraries. This could be especially useful on more generic cloud infrastructure, such as what Amazon offers, where network capability and topology is much less conducive to HPC communication compared to a purpose-built supercomputer.

While the technology Samplify is offering is not a panacea for all these data bottlenecks, it has the potential to make a significant dent in throughput and storage. Right now the company is in the process of collecting proof points for the technology. According the Wegener, APAX has been validated by two Samplify investors: Schlumberger, an oil & gas exploration firm, and Mamiya, a Japanese manufacture of high-end digital cameras. In the case of Schlumberger, the technology is being used in its software incarnation, while Mamiya has inserted the APAX into its FPGA chips. Other trials are in process with seismic and multiphysics customers, but the company is not willing to name names at this point.

Samplify envisions a market for APAX in high performance and cloud computing as well as at the other end of the IT spectrum in mobile computing devices and consumer electronics. The company estimates a total addressable market of $700 million by 2014: $370 million for APAX IP blocks (Verilog RTL) on 1.8 billion devices and $330 million for APAX software on 16.7 million cores.

As of this week, the APAX technology is ready to ship in software form. The hardware IP block will be available for licensing in the middle of the year. Pricing has not been disclosed.

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!

InfiniBand Still Tops in Supercomputing

July 19, 2018

In the competitive global HPC landscape, system and processor vendors, nations and end user sites certainly get a lot of attention--deservedly so--but more than ever, the network plays a crucial role. While fast, perform Read more…

By Tiffany Trader

HPC for Life: Genomics, Brain Research, and Beyond

July 19, 2018

During the past few decades, the life sciences have witnessed one landmark discovery after another with the aid of HPC, paving the way toward a new era of personalized treatments based on an individual’s genetic makeup Read more…

By Warren Froelich

WCRP’s New Strategic Plan for Climate Research Highlights the Importance of HPC

July 19, 2018

As climate modeling increasingly leverages exascale computing and researchers warn of an impending computing gap in climate research, the World Climate Research Programme (WCRP) is developing its new Strategic Plan – and high-performance computing is slated to play a critical role. Read more…

By Oliver Peckham

HPE Extreme Performance Solutions

Introducing the First Integrated System Management Software for HPC Clusters from HPE

How do you manage your complex, growing cluster environments? Answer that big challenge with the new HPC cluster management solution: HPE Performance Cluster Manager. Read more…

IBM Accelerated Insights

Are Your Software Licenses Impeding Your Productivity?

In my previous article, Improving chip yield rates with cognitive manufacturing, I highlighted the costs associated with semiconductor manufacturing, and how cognitive methods can yield benefits in both design and manufacture.  Read more…

U.S. Exascale Computing Project Releases Software Technology Progress Report

July 19, 2018

As is often noted the race to exascale computing isn’t just about hardware. This week the U.S. Exascale Computing Project (ECP) released its latest Software Technology (ST) Capability Assessment Report detailing progress so far. Read more…

By John Russell

InfiniBand Still Tops in Supercomputing

July 19, 2018

In the competitive global HPC landscape, system and processor vendors, nations and end user sites certainly get a lot of attention--deservedly so--but more than Read more…

By Tiffany Trader

HPC for Life: Genomics, Brain Research, and Beyond

July 19, 2018

During the past few decades, the life sciences have witnessed one landmark discovery after another with the aid of HPC, paving the way toward a new era of perso Read more…

By Warren Froelich

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

AI Thought Leaders on Capitol Hill

July 14, 2018

On Thursday, July 12, the House Committee on Science, Space, and Technology heard from four academic and industry leaders – representatives from Berkeley Lab, Argonne Lab, GE Global Research and Carnegie Mellon University – on the opportunities springing from the intersection of machine learning and advanced-scale computing. Read more…

By Tiffany Trader

HPC Serves as a ‘Rosetta Stone’ for the Information Age

July 12, 2018

In an age defined and transformed by its data, several large-scale scientific instruments around the globe might be viewed as a ‘mother lode’ of precious data. With names seemingly created for a ‘techno-speak’ glossary, these interferometers, cyclotrons, sequencers, solenoids, satellite altimeters, and cryo-electron microscopes are churning out data in previously unthinkable and seemingly incomprehensible quantities -- billions, trillions and quadrillions of bits and bytes of electro-magnetic code. Read more…

By Warren Froelich

Tsinghua Powers Through ISC18 Field

July 10, 2018

Tsinghua University topped all other competitors at the ISC18 Student Cluster Competition with an overall score of 88.43 out of 100. This gives Tsinghua their s Read more…

By Dan Olds

HPE, EPFL Launch Blue Brain 5 Supercomputer

July 10, 2018

HPE and the Ecole Polytechnique Federale de Lausannne (EPFL) Blue Brain Project yesterday introduced Blue Brain 5, a new supercomputer built by HPE, which displ Read more…

By John Russell

Pumping New Life into HPC Clusters, the Case for Liquid Cooling

July 10, 2018

High Performance Computing (HPC) faces some daunting challenges in the coming years as traditional, industry-standard systems push the boundaries of data center Read more…

By Scott Tease

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This