Exploring GPU acceleration with cuDF, cudf. pandas, and the Polars GPU Engine The post How Much of a Data Science Workflow Can Run on a GPU Today?
arXiv:2608.21806v1 Announce Type: cross Abstract: Computational resources are increasingly central to NLP research, but how closely reported GPU capability aligns with scholarly impact remains unclea...
Why “average utilization” lies about how full your GPUs really are The post When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI appeared first on Towards Data Science .
Hint: it is not GPU speed! The post The Real Challenge Limiting AI Models Today appeared first on Towards Data Science .
arXiv:2601. 20115v3 Announce Type: replace-cross Abstract: As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research.
GPU-CFR compiles a fixed game into static dataflow, eliminating per-iteration kernel launches and reducing framework operations by up to 18.1×. On an A100 GPU it achieves 29.8–80.4× speedups over the fastest prior GPU CFR and 14–258× over the LiteEFG CPU implementation for large games. The compiled representation alone delivers 2.2–51.1× acceleration on eight CPU threads, while the CUDA Graph Replay enables a single graph launch per iteration.
The PCIe transfer latency is silently bottlenecking your agentic inference. Here is how building a custom device-resident vector search kernel bypasses the CPU to unlock deterministic microsecond tail latencies.
GPU-CFR is a compiler and runtime that transforms any counterfactual regret minimization (CFR) game into a static dataflow representation, eliminating variable kernel launches by precomputing indices, flat arrays, and depth‑level execution blocks. This approach reduces framework operations by up to 18.1× and allows a single CUDA Graph Replay to execute each iteration, yielding 29.8–80.4× speedups over the fastest prior GPU CFR on an A100 and 14–258× over the LiteEFG CPU implementation for large games. The compiled representation alone delivers 2.2–51.1× acceleration on eight CPU threads, while the optimized path reproduces reference iterates exactly and pays for its overhead within the first solve.
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination.
arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.