Lots of researchers who do computationally intense work could use more processing power. Many of them actually have that power available on their computers, but haven’t found a way to take advantage of it. The computational clout is in the multiple processor cores of the computer’s graphics system, where it is not easily accessible.

A tool like NVIDIA’s CUDA parallel computing model makes the GPU cores, up to 240 of them on the latest NVIDIA Tesla GPUs, available to programs. But to take maximum advantage of it, you have to be a skilled C or C++ programmer. The problem is that many of the people who would benefit most from high-performance computing are not software developers by profession. They write customized code out of necessity, but their primary work is in chemistry, geology, astronomy, physics or biology.

Tech-X Corp., a Boulder, CO, software and consulting company specializing in high-performance scientific computing, is working to change that. Its GPUlib is a tool that brings GPU-based computing into the high-level tools used by researchers, including ITT Visual Information Solutions’ IDL, Mathworks’ MATLAB, and that trusty old laboratory standby, Fortran.

“Parallel computing used to be a very elite field,” says Peter Messmer, vice president for space applications at Tech-X. “Few applications are designed to take advantage of it. GPU processing makes it much more mainstream.” Until GPU processing came along, the cheapest way to get very high performance in the lab was by building a cluster of relatively inexpensive PCs, but this took skills that researchers who weren’t computer scientists or electrical engineers often lacked. “The GPU makes it much more mainstream,” says Messmer.

This post is an introduction to a series of reports on computer scientists and other researchers who are unlocking the high-performance computing potential of parallel programming using large numbers of processor cores. But first some background on the opportunity and the challenge of parallel computing.

Some time around the middle of the last decade, the race to ever-faster computing hit the wall. Until then, designers had delivered soaring performance through three well-understood technologies: shrinking the already microscopic transistors, cramming more of them into each processor, and running them at higher speeds

The problem was that faster processor performance translated into higher power consumption and more heat, and even if you could find a way to get rid of the excess heat before the chips fried, continuation of the trend posed unacceptable economic and environmental costs.

An alternative route to faster computing had been around for some time. Instead of driving the processors harder, use more of them.  Mainframe computers and servers had long used multiple processors to handle heavy loads, but advances in chip technology made it possible to combine multiple processors on a single chip, an approach that is both more efficient and much cheaper. Today, high-performance computing is a story of dividing computational workloads over multiple processor cores. In the case of personal computers, this means both a handful of cores in the CPU and dozens, sometimes hundreds of cores in the graphics processing unit (GPU).

How can GPUs help women who are facing a possible diagnosis of breast cancer? The same computational speed-ups that GPUs bring to everything from oil exploration to drug discovery are also taking place in the field of medical imaging. In the case of breast imaging, TechniScan Medical Systems is using parallel computing to give women and their doctors fast, accurate information about breast health.

Breast cancer detection today relies on 2D imaging systems and invasive procedures like biopsy. The former is often imprecise due to various factors such as operator expertise, the latter is painful and often unnecessary. But because doctors don’t want to be wrong on something like cancer, they’ll act aggressively on anything that looks suspicious – even though 80% of biopsies come back negative. That’s a lot of unnecessary fear and distress for patients.

TechniScan’s solution uses 3D ultrasound to create a detailed picture that may help doctors in the process of finding and treating breast cancer. (This video gives a detailed look at how the system works.) Basically, the machine’s scanner rotates all the way around a patient’s breast, capturing a scan every 2 degrees, and then composites a detailed 3D image. Each image is around 8 to 9 million voxels (the 3D equivalent of a pixel) and requires more than 120 million Fast Fourier Transform (FFT) calculations to build.

This post is an entry in The World Isn’t Flat, It’s Parallel series running on nTersect, focused on the GPU’s importance and the future of parallel processing. Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. For more information on GPUs and their applications, keep your eyes on The World Isn’t Flat, It’s Parallel.

Every time I hear about some little boy or girl who has a life-threatening disease, I get reenergized to promote the opportunities that computers offer in helping reduce disease and finding cures for human ailments. The exciting new developments on this frontier have to do with using parallel computing to speed up research and create the breakthrough discoveries that will save lives.

Finding new drugs is a complex and laborious task. Biochemists have to try millions of compounds before they can figure out which ones are effective against a particular virus or bacteria or which cause a desired reaction in the human body. To narrow the field, scientists use automated tools for high-throughput screening. But at some point, they have to test the remaining biochemical compounds in time-consuming manual experiments in a “wet” laboratory. (See our recent piece on the Tesla BioWorkbench for more information on how we’re helping researchers in the life sciences use GPU computing in their work.)

This post is an entry in The World Isn’t Flat, It’s Parallel series running on nTersect, focused on the GPU’s importance and the future of parallel processing. Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. For more information on GPUs and their applications, keep your eyes on The World Isn’t Flat, It’s Parallel.

The World Is Parallel has covered serious ground so far, discussing the impact of parallel computing on municipalities planning for natural disasters and on oil and gas companies deciding where to sink $100 million wells.

Now, we’re going to touch on how the film industry is tapping into parallel computing to create the next generation of stunning films.

Director James Cameron has said repeatedly that “Avatar” represents for him his life’s achievement. And it was no less an undertaking for the visual effects vendors that were tasked with creating the stunningly realistic 3D world of Pandora.

Wellington, NZ-based Weta Digital served as the primary visual effects vendor on “Avatar,” building scenes with as many as 800 computer-generated characters in lush, rich environments. In a first for the film industry, certain visual effects for “Avatar” took billions of polygons (the surfaces of the shapes that make up a 3D world). The unprecedented complexity of the visual sequences required the development of a proprietary ray-tracing program to handle the bulk of calculations before final rendering.

This post is an entry in The World Isn’t Flat, It’s Parallel series running on nTersect, focused on the GPU’s importance and the future of parallel processing. Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. For more information on GPUs and their applications, keep your eyes on The World Isn’t Flat, It’s Parallel.

Many people don’t realize that the parallel computing revolution that’s transforming science and industry is also having an effect much closer to home. By allowing state and local governments to make use of sophisticated data analysis, parallel computing is changing the way they do everything from plan for disaster to fight crime.

One of the most powerful tools for local planners and agencies is Geographic Information Systems (GIS) software, which analyzes satellite imagery by applying complex image processing filters. GIS data can help city planners determine emergency plans, and even set recovery and rebuilding efforts at a natural disaster.

But analyzing this complex data is a resource-intensive process. With the parallel processing power of NVIDIA CUDA and GPUs, local governments can unlock the practical value of GIS data much more quickly and cost-effectively than with CPUs alone.

This post is an entry inThe World Isn’t Flat, It’s Parallel series running on nTersect, focused on the GPU’s importance and the future of parallel processing. Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. For more information on GPUs and their applications, keep your eyes on The World Isn’t Flat, It’s Parallel.

For oil and gas companies, finding the precise location to drill can be worth hundreds of millions of dollars. Dig a few feet in the wrong direction, and over time the loss in production can mean significant lost revenue.

With stakes this high, it’s critical that companies involved in oil and gas exploration use the most advanced seismic imaging technology available. Increasingly, this compute-intensive work is being done with GPUs.

SeismicCity, a Houston-based leader in depth imaging services, uses an NVIDIA Tesla S1070 system to create incredibly precise images of geological data from deep inside the earth. The resulting models enable oil and gas companies to more quickly and accurately discover new oil and gas reserves.

SeismicCity made the change to GPUs a little over a year ago when it replaced its CPU-based data center. Not only did the switch produce a 10-fold performance boost, but it also allowed SeismicCity to increase the complexity of its algorithms, giving it a distinct competitive advantage. It developed a proprietary technique – Reverse Time Migration (RTM) – that is one of the most advanced seismic imaging technologies in the industry.

This post is an entry inThe World Isn’t Flat, It’s Parallel series running on nTersect, focused on the GPU’s importance and the future of parallel processing. Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. For more information on GPUs and their applications, keep your eyes on The World Isn’t Flat, It’s Parallel.

The CPU (central processing unit) has often been called the brains of the PC. But increasingly, that brain is being enhanced by another part of the PC – the GPU (graphics processing unit), which is its soul.

All PCs have chips that render the display images to monitors. But not all these chips are created equal. Intel’s integrated graphics controller provides basic graphics that can display only productivity applications like Microsoft PowerPoint, low-resolution video and basic games.

The GPU is in a class by itself – it goes far beyond basic graphics controller functions, and is a programmable and powerful computational device in its own right.

This post is an entry inThe World Isn’t Flat, It’s Parallel series running on nTersect, focused on the GPU’s importance and the future of parallel processing. Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. For more information on GPUs and their applications, keep your eyes on The World Isn’t Flat, It’s Parallel.

Reading a hot best-seller is a serial process. Start at the beginning and read it to the end. But a task like counting the number of vowels in that same book can best be done as a parallel process. Give each paragraph to a different person, and it gets done far more quickly.

So it is with computing. Some tasks lend themselves to serial computing. But the complexity and data processing requirements that underlie our most challenging problems are rapidly moving beyond the capacity of serial processing. We’ve all gotten used to Thomas Friedman’s idea that the world is flat. In solving problems with computers, we’ve similarly accepted the assumption that the world is serial.

In fact, the world is parallel.

Technology reflects the thinking in force at the time of its creation. And over time, it reflects our own self-imposed limitations. At some point, they have to be eclipsed.