Why GPUs Might Not Be the Only Game in Town for Deep Learning

modern deep learning, GPU kernels have long served as the backbone, providing the necessary computational muscle. Yet, optimizing these kernels is far from trivial, often demanding a painstaking process of evolutionary search or the deployment of coding agents, both of which necessitate repeated measurements on target hardware. The irony is that while these measurements deliver the ground-truth signal vital for kernel searches, they're anything but cheap. Each kernel evaluation implies a cycle of compilation followed by multiple executions on a GPU, an endeavor that can drain resources.

The Bottleneck

Today's advances in large language model (LLM) inference are tipping the scales. They're driving down the costs associated with writing novel kernels, and as LLM-driven searches ramp up to engage in larger search budgets, on-device evaluations emerge as a significant bottleneck. This challenge raises an intriguing question: can LLMs step up as credible GPU surrogates for kernel evaluation? The idea is for LLMs to predict the performance of proposed kernels, thus potentially sidestepping the need for exhaustive physical GPU tests.

For an LLM to serve effectively as a surrogate, it needs to do more than just make accurate forecasts. It must also be aware of its limitations. In other words, an ideal surrogate should recognize when it might falter and defer to the GPU for confirmation. This self-awareness, if you'll, is important.

Reinforcement Learning's Role

Enter reinforcement learning. Studies indicate it can refine the LLM's forecast accuracy and bolster its confidence calibration. In practice, experiments have shown that LLMs can't only predict relative kernel performance with a respectable degree of precision, but their utility is further enhanced through reinforcement learning. The practical impact is tangible: within a kernel search, using a surrogate means considering multiple times the number of candidates without exceeding the same GPU evaluation budget. This efficiency often leads to the discovery of faster kernels compared to a baseline with an equivalent budget.

But here's the crux: relying on LLMs as GPU surrogates opens up intriguing possibilities. They could evolve into virtual models of a GPU, fundamentally altering their role from mere kernel generators to key players in kernel optimization. Color me skeptical, but isn't it time we questioned the entrenched reliance on GPUs for every step of deep learning's journey? Could a shift toward LLM surrogates indicate a broader change in the methodology of computational efficiency?

Looking Ahead

What they’re not telling you: the future could see LLMs reduce the dependency on GPU-heavy processes, ushering in a new era of deep learning where efficiency reigns supreme. As we stretch the limits of computational capabilities, the role of LLMs in offloading tasks traditionally reserved for GPUs could reshape kernel optimization. This shift won't just influence how models are trained but could redefine the economic calculus of computational resources in AI research. Let's apply some rigor here, and start viewing LLMs not as mere tools but as strategic assets in the optimization arsenal.