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Nvidia CUDA : practical uses
by Damien Triolet
Published on August 17, 2007

Other competitors
Nvidia and AMD aren’t the only ones to try out the massively parallel processor market. IBM (as well as Sony but most likely to bring attention to the PS3 than anything else) propose a BladeCenter based on two Cell processors. You may recall, the Cell is composed of a generalized core accompanied by a block of 8 cores devoted to parallel calculation. IBM already offers an entire development environment around this platform. Compared to a GeForce 8800, a Cell has fewer calculation units and less memory bandwidth, but offers a higher frequency and more local memory for calculation units (256 KB versus 8-16 KB for a GeForce 8).


IBM commercializes the BladeCenter equipped with two Cell processors

Intel is also involved in this domain with the Larabee project, which is a massively parallel chip destined to serve as a GPU as well as a coprocessor. In its presentations, which are supposed to be confidential, Intel mentions 16 to 24 cores which have 512 bit SSE units (or sixteen 32 bit operations per cycle and per core!). Each of these cores has an L1 cache of 32 KB and an L2 of 256 KB, all accompanied by an overall cache of 4 MB. Larabee is expected in 2009 or 2010 and will have its big advantage of being based on x86 architecture.


The calculation units of each architectures act differently
What utility?
But what is the utility of these calculation units? It's not for gaming and not to accelerate Internet. By this we mean, at least for now, it’s not for general public use but rather professional. A number of scientific applications need enormous calculation power that no generalized processor can provide. The solution therefore is to create huge supercalculators based on hundreds or even thousands of processors.

The conception of these calculators is very long, complex and expensive. Where a massively parallel processor is effective, it will allow at an identical cost/bulkiness to offer much more calculation power or to offer the same capabilities at less cost.

In our opinion, there is no current plan to build an immense supercalculator based on GPUs. They will first have to prove themselves and mature because there is no reason to take risks at this level. Moreover, it would be more interesting if Nvidia would give more information on the reliability of its chips, error rates (the memory isn’t ECC for example), crash rates, etc. Asked about this at each announcement involving stream computing, Nvidia has never been able to answer. It would appear this has been neglected, perhaps voluntarily, because marketing prefers not to make public this type of data. We should not stop thinking that the GeForce never breaks down or never makes any errors.


Current use is more about putting into place systems that were thought to be impossible without such chips. We can't imagine a supercalculator in each hospital unit, for example. An accelerator such as the Tesla could allow a work station to carry out tasks that were unimaginable before. Or to carry out operations in real time that can be very long.

In a press conference at the end of May, Nvidia invited several partners who demonstrated some practical uses of GPUs.

Acceleware is a company which develops platforms based on GPUs destined to accelerate a certain number of functions. The platform is used by its customers to accelerate their application. Acceleware showed us a demo of simulated impact of radiation emitted from a GSM on human tissues as well as citing other uses, for example, for rapid detection of breast cancer or simulations related to pacemakers.


Evolved Machine is a company which is trying to understand the functioning of neurons, in order to be able to reproduce the circuits and create systems capable, for example, of learning and recognizing objects or odors as humans do naturally. Simulating a single neuron represents the evaluation of 200 million differential equations per second. When we know the basic structure of neurons represents thousands of them, we can easily imagine the enormous amount of calculations to process. Evolved Machine indicates having seen a gain of 130x in processing speed by using GPUs and is working on the development of a rack of GPUs which will be capable of competing with the best supercalculators in the world at 1/100th their cost.


Headwave develops solutions for the analysis of geophysical data. Petroleum companies are finding it more and more difficult to find oil and gas reservoirs. The deeper they are detected the harder is their analysis. An enormous quantity of data needs to be gathered and then processed. This processing is so heavy that the data collected accumulates at lightning speed and cannot be analyzed due to a lack of calculation power. The use of GPUs allows significantly accelerating this process, notably by making it possible to display results in real time. According to Headwave, the infrastructure to take advantage of GPUs is already in place and petroleum companies are ready and impatient to use this technology.


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