CUDA Spotlight: GPU-Accelerated Science and Computing
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This week's spotlight is on Dr. Vincent Natoli, president and founder of Stone Ridge Technology.
Dr. Natoli is a computational physicist with 20 years of experience in high performance computing. Previous roles include senior physicist at ExxonMobil Corporation and Technical Director at High Performance Technologies Inc. (HPTi).
NVIDIA: Vincent, tell us a bit about Stone Ridge Technology.
NVIDIA: What services do you provide?
It used to be easier to be both an expert in your science domain and able to do a decent job writing simulation code when people were writing mainly scalar Fortran code. It’s much more complex today with multi-core, multi-node cluster solutions, GPU computing and object oriented languages. It’s a rare person who can do both well and those are the people who I hire!
NVIDIA: Why is GPU computing so relevant today?
NVIDIA: What kinds of applications benefit the most from GPU computing?
NVIDIA: What advice do you have for a developer who hasn’t yet made the leap to GPU computing?
NVIDIA: What advice would you offer to CIOs who are looking at adopting GPU computing in their organizations?
For a new technology to even get on the radar, it should offer superior performance, it should not depend on the HPC market exclusively for its success, and it should have a mature development environment. GPUs qualify on all three fronts.
On the issue of development environment, NVIDIA deserves a lot of credit for delivering CUDA to the community. I’ve said before that the most enduring contribution to HPC from GPU computing may well be the CUDA programming model.
NVIDIA: Tell us about the ROI of using GPUs in a heterogeneous computing environment, in terms of speedup, man months, etc.
Cost savings are realized by getting answers more quickly and in reducing infrastructure footprint and power budget. That savings is worth differing amounts to a firm in finance, oil and gas or bioinformatics. On the cost side there are direct costs like the hardware and code ports and indirect costs like potential inefficiencies introduced by changes to the IT infrastructure. For a data point on the cost of porting, optimizing, validating and integrating code, our projects range in duration from three to nine months.
NVIDIA: As computing becomes more powerful, what will the future hold?
The near term three-to-five year timeframe is more accessible to prognostication. With respect to general computing, I believe the cloud will dominate. The economies of scale there make so much sense. Computing will become more like a utility to which you subscribe similar to the way we subscribe now to music and video.
In the HPC realm I believe the trend of leveraging easily-accessible systems from industry leaders like NVIDIA will continue. I am not as enthusiastic about the convergence of CPU and GPU architectures as others who see it around the corner. In the implementations I’ve seen, the integration basically throws out all the advantages of GPUs by reducing the number of cores and the memory bandwidth.
I believe there will continue to be advances in data parallel programming models such as CUDA which will allow developers to focus on writing optimal kernel code that scales more transparently. Finally, I see increasing attention and emphasis on power/FLOP and power/Byte as we move to larger and larger systems.