Optimizing Data Transfer in AI/ML Workloads

What’s Happening

Alright so A deep dive on data transfer bottlenecks, their identification, and their resolution with the help of NVIDIA Nsight™ Systems The post Optimizing Data Transfer in AI/ML Workloads appeared first on Towards Data Science.

In a typical AI/ML workload , a deep learning model is executed on a dedicated GPU accelerator using input data batches it receives from a CPU host. Ideally, the GPU — the more expensive resource — should be maximally utilized, with minimal periods of idle time. (we’re not making this up)

In particular, this means that every time it completes its execution on a batch, the subsequent batch will be “ripe and ready” for processing.

The Details

When this does not happen, the GPU idles while waiting for input data — a common performance bottleneck often referred to as GPU starvation. , see A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline ), we discussed common causes of this issue, including: inefficient storage retrieval, CPU resource exhaustion, and host-to-device transfer bottlenecks.

In this post, we zoom in on data transfer bottlenecks and revisit their identification and resolution — this time with the help of NVIDIA Nsight™ Systems (nsys) , a performance profiler designed for analyzing the system-wide activity of workloads running on NVIDIA GPUs. PyTorch Profiler Readers familiar with our work may be surprised at the mention of NVIDIA Nsight profiler rather than PyTorch Profiler .

Why This Matters

In our previous posts we have advocated strongly for the use of PyTorch Profiler in AI/ML model development as a tool for identifying and optimizing runtime performance. Time and again, we have demonstrated its app to a wide variety of performance issues. Its use does not require any special installations and can be run without special OS permissions.

The AI space continues to evolve at a wild pace, with developments like this becoming more common.

The Bottom Line

Its use does not require any special installations and can be run without special OS permissions. NVIDIA Nsight profiler, on the other hand, requires a dedicated system setup (or a dedicated NVIDIA container ) and — for some of its features — elevated permissions, making its use less accessible and more complicated than PyTorch Profiler.

Is this a W or an L? You decide.