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Tesla Computing |
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Putting those FLOPS to PRODUCTIVE Use |
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by Josh Walrath |
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The Software Side NVIDIA released the beta of the CUDA software this past February, and they have been working with the GPGPU community to improve support. Initially there were a lot of complaints about how hard it was to actually program effectively for CUDA, but over time those complaints are looking to have died down. NVIDIA has been working with a variety of entities to further develop their applications to work with the Tesla products. Companies such as Acceleware are helping to develop solutions aimed at the geophysical and electromagnetic simulation. NVIDIA’s goal is to be able to program these GPGPU applications in C without any issues, and with good performance. NVIDIA claims that it is at a point where it can offer these products and have the tools available and mature enough to enable a wider spectrum of applications to be ported for use in the Tesla products. The Future of Tesla Currently the Tesla products utilize the G80 processors for their products. There are no plans to utilize the lower end G84 and G86 products. This is mainly because it would take about 4 G84 chips to equal the overall performance of a single G80. In high performance computing, power is not so much the issue as is space. Being able to use a 1U rackmount to house 4 C870s vs. having that same computing power with slightly better power consumption with 16 G84 based units is a big deal. So for the time being we will only see the G80 based products in the market. This does not rule out the potential for some small form factor usages for the G84, as I would imagine that it could be handy for workstation class laptops. Being able to plug in an Expresscard version of a Tesla product based on G84 could be appealing to some users, but likely that market is so incredibly small that it is not worth it to develop. The G80 does have one large potential disadvantage over other solutions. It currently supports single precision floating point operations. Applications with require double precision operations will obviously not work on the Tesla series of products. By the end of this year NVIDIA does expect to introduce a new Tesla based product that does support double precision operations. This will likely be based on the 65 nm G80 replacement, but with a few changes. At first glance one would honestly think to themselves, “How do double precision floating point operations help my Unreal Tournament 3 performance?” The quick and correct answer is that it does not help one bit. But the upcoming GPUs that will go into the Tesla products later this year featuring double precision will not be the same GPUs that will go into retail boards this Holiday Season. NVIDIA is looking to differentiate their products at the chip level, so that means two separate 65 nm parts that are still based on the same overall architecture, but with one supporting double precision and the other does not. This does not mean that NVIDIA has had to hire double the engineers to complete such a design. Due to the modular nature of current GPUs, it is not as daunting a task to introduce new products based on the same general architecture. We see this currently with the whole range of products that NVIDIA ships. Modern EDA software and standard cell design rules allow engineers to lay out and verify a new design based on the current architecture a lot faster than they used to be able to. Other tools such as the FPGA prototyping machines help to accelerate this process by exposing flaws in timing that could be missed by the EDA software. Later this year we will see NVIDIA release their 65 nm desktop chips for the high end, and around the same time there will be a standalone Tesla product generally based on that same architecture, but supporting double precision operations. NVIDIA realizes that it probably cannot survive forever by just producing graphics chips. By diversifying their product lines to include HPC initiatives, they are expanding their product base as well as providing more security for themselves in the future if the standalone graphics market is rendered obsolete. This is merely the starting point of a new product line and a new market. The potential of GPGPUs in High Performance Computing is largely untapped, but with NVIDIA making a strong push into this market it will likely become commonplace in the very near future. AMD is also pushing hard into this market, and they will likely take the same approach as NVIDIA is doing. The differentiation, and the potential benefits such work will likely inspire, will help these companies through difficult times when a product from one side or the other falls down or does not live up to expectations. What will likely be the determining factors of success in the next year for this budding industry are the software support and the presence of products supporting double precision operations. So far NVIDIA has the upper hand with products available, as well as a clear roadmap for both its hardware and software support. Something else that should be mentioned is that CUDA support is not only relegated to the Tesla series of products. Software written for CUDA should work on any G8x class product, but users should probably only consider using the full 8800 GTX to avoid any compatibility issues. This means that any person that has a need for this type of computing can go down to the local brick and mortar and buy a 8800 GTX for around $550, slap it into their workstation, and have a fully functional board with the same features as the more expensive Tesla models (albeit with ½ the memory).
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