Mathematica 8 Gets Performance Boost, Integration with Wolfram Alpha

By Michael Feldman

December 14, 2010

The eighth version of Mathematica was released last month, the latest in Wolfram Research’s 22-year-old computational software platform. Although the tool is a relative newcomer to the high performance computing world, a raft of new capabilities have been added over the last several years that are aimed directly at the performance crowd. Mathematica 8 builds on those capabilities and adds some new ones that make it a serious contender for HPC applications.

Focus on performance started in earnest with Mathematica 4 back in 1999. Beginning with that release, Wolfram Research started to incorporate a variety of features and capabilities that improved runtime execution, including optimizing algorithms, supporting linkage of external C and Fortran libraries, and adding the ability to compile code. In 2008, with Mathematica 7, built-in support for multicore parallelism and compute clusters was added.

Mathematics 8 adds a number of new features that should boost performance even further. Perhaps the most important is the ability to generate, compile and link C code. The new feature allows Mathematica code to be automatically translated into C source code. The source can then be driven through a standard compiler (requiring a native Windows or Mac C compiler) and linked into a Mathematica executable for production.

The idea here is to take advantage of the speed of C-compiled code to boost performance of critical pieces of the Mathematic program. Previously Mathematica only supported compilation to a Java-like virtual machine byte-code, which although faster than interpreted execution, tended to be a good deal slower than compiled C code. In one example, a rendering application using vanilla Mathematica delivered just one frame every 10 to 15 seconds, while C compiled code was able to achieve two to four frames per second. Comparable speedups are to be expected from similar compute-intensive codes. All of this can be accomplished without the programmer ever having to write a single line of C.

Better yet, a parallelization option can be applied to a compiled function, which Mathematica will use to create a multi-threaded implementation. This can speed execution even further, assuming of course that the target CPU is multicore.

Compiled C code can be collected in dynamic link libraries (DLLs), which can be sucked back into the application or shared with other Mathematica programs. The ability to link DLLs also means externally developed C and C++ libraries can be incorporated into Mathematica, opening the door to many more performance optimized packages. Prior to this, talking with external C code involved the MathLink interface, which was burdened with the overhead of inter-program communication. Being able to access DLL routines directly makes calling external code much more efficient and straightforward.

For the GPGPU enthusiast, Mathematica 8 brings in support for CUDA and OpenCL. Unfortunately, this feature doesn’t have the seamless automation offered by the C code generation capability. Rather, the targeted algorithm has to be developed in CUDA or OpenCL first and then folded into the program later. Basically, Mathematica automates some of the housekeeping functions, such as downloading code and data to the GPU card, and uploading the results back to the host. GPGPU support can be scaled to utilize all the GPUs on a system, or, using the gridMathematica add-on, across a cluster.

Although you can’t automagically transform an arbitrary function into a GPU version, Mathematica 8 does include a couple dozen built-in functions that are already optimized for CUDA-enabled GPUs (in other words, those from NVIDIA). The functions are spread out across linear algebra, financial simulation, and image processing. The folks at Wolfram Research will undoubtedly be adding more built-in GPU routines in future versions, while also promising a more streamlined approach for GPU support.

Another category of performance improvements is enabled by speedups to a number of core algorithms. These include optimized solvers for integer linear algebra, highly oscillatory functions, transcendental and high-degree polynomial methods, and a number of new special functions. In some cases, the optimizations can boost performance by an order of magnitude or more, depending upon the size of the problem.

Besides the additional performance-boosting capabilities, Mathematica 8 also includes about 500 new built-in functions — an increase that represent nearly the entire function count in the original Mathematica 1 of 1988. The new capabilities in version 8 encapsulate high-level symbolic functions for probability and statistics; permutations and group theory algorithms; financial engineering routines of general utility; control system functions; wavelet analysis functions; graph and network algorithms; and image processing routine.

The last category encompasses some very useful routine for processing visual data. One of the new capabilities is feature detection, such as facial and character recognition. Also included are geometric transformations and image alignment. For video, Mathematica can now import and export individual frames as well as do real-time capture of webcam streams. All of these capabilities can be combined to deliver some rather sophisticated image processing applications on top of an already full-featured computational engine.

Perhaps the most visible addition to version 8 — at least from a user interface point of view — is the integration with Wolfram Alpha, the company’s Web-based computational knowledge engine. There are a number of advantages to marrying Mathematica to its Web spinoff, which, by the way, is itself a Mathematica application at its core.

First is the ability to tap the store of curated data in Wolfram Alpha, which encompasses a large and growing database that spans many technical and non-technical disciplines. It remains to be seen whether giving Mathematica users access to Wolfram Alpha data spurs new applications or will just be used as a sandbox for more customized data-centric applications.

For the application designer, one of the most potentially interesting uses of Wolfram Alpha is the ability to use its free-form linguistic capabilities. So instead of having to define a problem within the strict confines of the Mathematic language, you can use (more or less) natural language. So, for example, summing all the integers from 1 to 1000 would have to be specified as Sum[i, {i, 1, 1000}] in Mathematica, but could be simply stated as “sum integers 1 to 1000′” using the free-form mode.

The English version is automatically converted to Mathematica syntax on the fly, which can then be tweaked and developed separately. Extending the capability a bit further, users can pass Mathematica variables into Wolfram Alpha calculations.

The nice thing about the Mathematica architecture is nearly all its features, including the new ones described here, are included in the core technology. The Wolfram Alpha team has shied away from toolboxes, libraries, and standalone product add-ons (with the exception of gridMathematica). As a result, the new version 8 features can immediately leverage the large foundation of accumulated Mathematica componentry.

In the kickoff for Mathematica 8 at the Wolfram Technology Conference in November, company CEO Stephen Wolfram reiterated his commitment to maintain the platform as a unified, consistent software tool. Keeping the architecture monolithic means they are free to evolve the product through refinement of the individual pieces and the addition of new ones. With this kind of model, the whole is always guaranteed to be greater than the sum of the parts. “We’ve had a very simple strategic methodology,” explained Wolfram. “Just implement everything.”

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!

DoE Awards 24 ASCR Leadership Computing Challenge (ALCC) Projects

June 28, 2017

On Monday, the U.S. Department of Energy’s (DOE’s) ASCR Leadership Computing Challenge (ALCC) program awarded 24 projects a total of 2.1 billion core-hours at the Argonne Leadership Computing Facility (ALCF). The o Read more…

By HPCwire Staff

STEM-Trekker Badisa Mosesane Attends CERN Summer Student Program

June 27, 2017

Badisa Mosesane, an undergraduate scholar who studies computer science at the University of Botswana in Gaborone, recently joined other students from developing nations around the world in Geneva, Switzerland to particip Read more…

By Elizabeth Leake, STEM-Trek

The EU Human Brain Project Reboots but Supercomputing Still Needed

June 26, 2017

The often contentious, EU-funded Human Brain Project whose initial aim was fixed firmly on full-brain simulation is now in the midst of a reboot targeting a more modest goal – development of informatics tools and data/ Read more…

By John Russell

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a Department of Energy sponsored collaboration between the Univ Read more…

By John Russell

HPE Extreme Performance Solutions

Optimized HPC Solutions Driving Performance, Efficiency, and Scale

Technology is transforming nearly every human and business process, from driving business growth, to translating documents in real time, to enhancing decision-making in areas like financial services and scientific research. Read more…

UMass Dartmouth Reports on HPC Day 2017 Activities

June 26, 2017

UMass Dartmouth's Center for Scientific Computing & Visualization Research (CSCVR) organized and hosted the third annual "HPC Day 2017" on May 25th. This annual event showcases on-going scientific research in Massach Read more…

By Gaurav Khanna

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “pre-exascale” award), parsed out additional information ab Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid whoops and hollers from the crowd, Thomas Sterling presented t Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out plans to push deeper into climate science and develop more gran Read more…

By John Russell

DoE Awards 24 ASCR Leadership Computing Challenge (ALCC) Projects

June 28, 2017

On Monday, the U.S. Department of Energy’s (DOE’s) ASCR Leadership Computing Challenge (ALCC) program awarded 24 projects a total of 2.1 billion core-hour Read more…

By HPCwire Staff

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a D Read more…

By John Russell

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid wh Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out pla Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big d Read more…

By Alex Woodie

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

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

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

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

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. 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

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 the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

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 Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

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

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 cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

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

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

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 advanced supercomputers. Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

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