 CCC Blog 
At the CCC’s Computing Futures Symposium in May, we had the privilege of hearing from Bill Dally, Chief Scientist and Senior Vice President of Research at NVIDIA, who delivered a compelling keynote address on the powerful synergy between government, academia, and industry. Dally captivated the audience with his personal journey through the technological revolutions that have shaped our world, driven by this collaborative model.
Dally’s central message was clear and resounding: government research, combined with private sector ingenuity, has had a significant impact on America. This investment yields two critical outcomes: it cultivates a deep pool of talent essential to U.S. industry, and it generates the breakthrough technologies that grant the United States and its companies unparalleled leadership in crucial sectors.
To illustrate this profound impact, Dally took the symposium attendees on a ride through NVIDIA’s rise, highlighting how the company stands at the forefront of the machine learning revolution’s hardware side. He pulled back the curtain on the magic behind tools like ChatGPT, explaining how transformer models, enormous datasets, and continuous fine-tuning are the bedrock of these powerful AI systems.
But how did we get here? Dally emphasized that while algorithms and data were crucial, the true “spark” that ignited the deep learning explosion in 2012 was the emergence of GPUs fast enough to tackle large models and datasets. He then delved into the incredible evolution of NVIDIA’s GPUs, detailing advancements such as:
- Number Representation: Moving from 32-bit to 8-bit, and even 4-bit, significantly boosted computational efficiency.
- Complex Instructions: The introduction of “tensor cores” (half-precision matrix multiply-accumulate instructions) dramatically amortized overheads, dedicating more energy to raw computation.
- Process Technology: While not the sole driver, process shrinks continued to contribute to performance gains.
- Sparsity: Efficiently handling sparse neural networks further optimized operations.
He revealed that these hardware innovations alone delivered an astounding 1,000x improvement in inference performance over a decade. But to reach the 10-million-fold leap in training cycles demanded by today’s massive models, parallelism was key. Dally explained how model parallelism (pipeline and tensor parallelism) and data parallelism, coupled with high-bandwidth interconnections like NVLink, allowed for the scaling required to train models on tens of thousands of GPUs.
Beyond the hardware, Dally underscored the critical role of software. He demonstrated how meticulously tuned software layers, evidenced by significant gains in MLPerf benchmarks, are as vital as the silicon itself in maximizing performance and usability.
The most compelling part of Dally’s talk was his personal timeline, illustrating the “tire tracks” of innovation. He shared how his early work on the Caltech Cosmic Cube and the MIT J-Machine, both DARPA-funded projects, laid the groundwork for efficient communication and synchronization in parallel computing. This fundamental research, coupled with his work on stream processing at Stanford (also funded by the government), directly influenced the development of GPUs.
Crucially, Dally emphasized the transfer of talent. He highlighted individuals like Ian Buck, who, after developing the Brook language (a precursor to CUDA) in academia, joined NVIDIA and, along with John Nickolls, co-created CUDA. Similarly, Andrew Ng and Brian Catanzaro, both trained through government-supported programs, played pivotal roles in demonstrating the power of GPUs for deep neural networks, laying the foundation for the CUDA Deep Neural Network (cuDNN).
“We get the technology, we put it in a student’s head, we ship the student,” Dally quipped, underscoring the invaluable pipeline of talent flowing from universities to industry.
In closing, Dally returned to his powerful central theme: the symbiotic relationship between government, universities, and industry. He warned that neglecting this pipeline, particularly in government funding for fundamental research, would lead to a decline in U.S. technological leadership, ceding future breakthroughs and dominant industries to rival nations.
Bill Dally’s keynote was a powerful reminder that the groundbreaking technologies we rely on today are not born in a vacuum. They are the product of decades of foundational research, nurtured by government investment, cultivated in academic institutions, and brought to fruition by visionary companies. It’s a formula for success that, as Dally eloquently articulated, we must continue to champion to maintain our edge in the global technological race.
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