Egyptian Startup Accelerates High Performance Accounting

By Michael Feldman

January 11, 2011

Although Egypt is not exactly the epicenter of high-end computing, a tech startup based in Cairo is looking to make its name in an emerging area of HPC. SilMinds has developed hardware accelerator technology designed to speed up a growing set of financial computing applications. The resulting products represent one of the few hardware-based solutions that support decimal floating point (DFP) math.

SilMinds was founded in 2007 as a research, design and consultancy firm, which is initially focused on providing industry standard IP cores for decimal floating point applications. The company’s chief technology officer, Professor Hossam A.H. Fahmy, was a member of the committee that formulated the IEEE 754-2008 standard for floating point arithmetic, including DFP. According to the SilMinds website, the first products were developed with grant money from the EU-Egypt Innovation Fund.

The company’s initial offering, SilAx, is a configurable vector DFP coprocessor implemented with FPGAs. The card can be equipped with either Altera or Xilinx FPGAs and hooked into any standard PCIe slot with at least four lanes. That makes it compatible with a wide array of x86 servers, HPC or otherwise.

No commercial deployments are yet claimed though. The company is currently talking with solution providers that deal directly with telecom and bank institutions, presumably with the idea of wrapping a complete solution around the SilMinds accelerator and offering it as a turnkey platform.

Keep in mind that decimal floating point operations are a bit of an outlier when it comes to computing. Most applications are performed using binary arithmetic, the natural style of number crunching for microprocessors. Decimal arithmetic can be performed with fixed-point (non-floating point) calculations, but the representations are too limited to support industrial strength money operations.

For example, adding $0.10 to $1.99 is fairly straightforward using fixed-point notation. But even doing something as simple as computing a 10 percent sales tax is problematic, given that 1/10 can only yield an approximate value when converted to binary. Where money is concerned, that’s not a good thing. Round-off errors add up and on a large scale can mean thousands or even millions of dollars end up in the bit bucket.

Decimal floating point, on the other hand, is able to support a much wider range of values than is available for fixed point, and provides much greater precision. Up until fairly recently, there was no encoding standard for DFP. But with the release of the IEEE 754-2008, there is now a vendor-independent specification for 32-, 64- and 128-bit decimal floating point representations and their behavior.

Give the regulatory laws imposed upon financial operations these days, DFP is the standard for nearly all applications in banking, telephone billing, tax calculation, currency conversion, insurance, and risk management. Now with the growing streams of real-time financial transactions zipping around the globe, performance and power efficiency have become looming issues. Some estimate that as much as one-third of the world’s server infrastructure is crunching financial data of some sort.

Demand for even more DFP capability appears poised to take off. Mobile networks are becoming ubiquitous across the globe, which should accelerate the need for real-time billing. Cell phones will soon be used as smart credit cards, able to initiate real-time payments at restaurants, movie theaters, and for a variety of other services (This is already in the works in Europe and Asia.). Smart energy grids are also being planned, which will require an extensive infrastructure to compute spot energy pricing. All these applications will require large-scale DFP.

How much demand actually exists for high performance DFP is anyone’s guess. But SilMinds is trying to position itself squarely in the path of this emerging space. So far, competition is minimal. Other than SilMinds, only IBM has decimal floating point implemented in hardware — in this case its z series computers (z9 and z10) as well as its Power6 and Power7 processors. But those solutions are rather expensive compared to a vanilla x86 server equipped with a SilMinds card.

Hardware is the key to performance, as well as power efficiency. Although DFP software libraries exist, they are relatively slow when it comes to compute-intensive DFP applications like large-scale telephone billing. SilMinds has tested its FPGA-based card solution using IBM’s Telco Billing benchmark and reported a 6X speedup compared to a software implementation on a 3 GHz x86 platform. “For other applications we expect that overall speedups will range from 4 to 5x up to 15x” said Assem El Gamal, SilMinds Design Manager. According to him, the variance depends on how much of the application is spent doing decimal floating point computations. In the case of the Telco benchmark, a fair amount of application run time is spent on disk I/O.

When looking at the performance of the DFP calculations in isolation, the results are even more impressive. SilMinds claims an 80X speedup for the core computation, with greater performance possible if the application can benefit from multiple cards.

Using an FPGA-based approach means the solutions can be customized to squeeze the optimal performance from the application. The hardware is implemented in VHDL code, which is designed, written and maintained by SilMinds. Customers tap into the low-level functionality of the accelerator via a set of provided application programming interfaces (APIs); they are not required to write any VHDL code themselves.

Multiple FPGAs per card and multiple card architectures are under study to support multiprocessing and virtualization, with many simultaneous application instances being afforded the maximum speedup needed by each to achieve maximum server resource savings. SilMinds speculates that datacenter TCO and energy saving could be reduced by 80-90 percent. Also under investigation is a network-centric acceleration architecture that could support SaaS and cloud computing.

A DFP ASIC is in the works as well, which according to SilMinds, has already been validated. The idea here is to get the ultimate in performance, sans the reconfigurability of the FPGA. Also on the horizon is a compiler that will generate the appropriate low-level parallel computations without the need for extensive API calls.

With other HPC technology focused on binary floating point capabilities to support scientific applications, the needs of performance-demanding DFP users have largely gone unserved. Financial regulatory requirements, a new floating point standard, and an expanding application space could propel SilMinds and their market into the limelight.

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!

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break “Speed Limit”

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

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. Read more…

By John Russell

HPE Extreme Performance Solutions

HFT Firms Turn to Co-Location to Gain Competitive Advantage

High-frequency trading (HFT) is a high-speed, high-stakes world where every millisecond matters. Finding ways to execute trades faster than the competition translates directly to greater revenue for firms, brokerages, and exchanges. Read more…

Intel Ships Drives Based on 3-D XPoint Non-volatile Memory

March 20, 2017

Intel Corp. has begun shipping new storage drives based on its 3-D XPoint non-volatile memory technology as it targets data-driven workloads. Read more…

By George Leopold

Researchers Recreate ‘El Reno’ Tornado on Blue Waters Supercomputer

March 16, 2017

The United States experiences more tornadoes than any other country. About 1,200 tornadoes touch down each each year in the U.S. Read more…

By Tiffany Trader

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 campaign. Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

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. 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 campaign. Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

AMD Expands Exascale Vision at IEEE HPC Symposium

March 7, 2017

With the race towards exascale heating up – for example, the Exascale Computing Program PathForward awards are expected soon – AMD delivered more details of its exascale vision at last month’s 23rd IEEE Symposium on High Performance Computer Architecture. The chipmaker presented an “Exascale Node Architecture (ENA) as the primary building block for exascale machine” including descriptions of component, interconnect, and packaging strategy along with simulation benchmarks to bolster its case. Read more…

By John Russell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

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 new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). 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 will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

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 was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Leading Solution Providers

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. 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 network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

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 campaign. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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