hgpu

Visual-to-Code generation, which transforms scientific plots, vector graphics, and webpages into executable scripts, demands a level of pixel-precise alignment that standard Multimodal Large Language Models (MLLMs) fail to achieve through Supervised Fine-Tuning (SFT) alone. While Reinforcement Learning (RL) offers a theoretical pathway to bridge this gap, its application is h...

We report a viability result: an entire Llama-class decoder, written in pure Rust and compiled to PTX by NVIDIA’s own experimental first-party Rust→PTX backend (cuda-oxide), runs FP4-quantized weights on a consumer NVIDIA RTX 5070 Ti and generates coherent English text. On a real TinyLlama-1.1B model quantized to MXFP4, the engine sustains roughly 181 tokens/s of decode throu...

Efficient inter-GPU communication is very important for scalable distributed deep learning, yet the internal behaviour of NVIDIA’s Collective Communication Library (NCCL) at the GPU kernel level remains largely unexplored. Existing profiling tools observe only host-side call boundaries, giving no visibility into the individual send, receive, and reduce steps that constitute e...

In the AI-driven era, NVIDIA CUDA libraries have become indispensable for accelerating compute-intensive tasks, yet their security assessment remains critically understudied due to closed-source code and unique programming paradigms. Existing efforts primarily target CUDA compiler vulnerabilities (e.g., NVCC), but overlook broader library-specific risks. This paper addresses ...

Emerging programming models like Triton enable developers to better exploit modern accelerators, but translating legacy code to Triton remains challenging. While Large Language Models (LLMs) show promise for code translation, they often generate incorrect or suboptimal implementations due to a lack of precise parallelization reasoning. We present TritonPilot, a compiler-assis...