The article introduces bpftime for GPU, a tool that extends eBPF's programmable, low‑overhead observation capabilities into GPU kernels, explains its implementation pipeline, compares its performance against Nsight and NVBit, and outlines future enhancements for GPU profiling.
This article walks through GPU performance analysis, starting with CUDA architecture fundamentals, demonstrating matrix multiplication optimization, explaining PTX assembly, and introducing the Neutrino framework for programmable GPU probes that enable fine‑grained, custom instrumentation and detailed timing measurements of kernel execution.
This article walks through the NVCC compilation pipeline, explaining how CUDA source files are transformed into host and device binaries, detailing file extensions, compilation stages, command‑line options, intermediate artifacts, and the role of registration functions such as __nv_cudaEntityRegisterCallback and __sti____cudaRegisterAll.
This article examines DeepSeek R1’s three breakthroughs, its low‑cost optimizations that bypass CUDA, and the resulting impact on the AI ecosystem, then provides a detailed technical review of seven open‑source reproductions—Open‑R1, Tiny‑Zero, SimpleScaling‑S1, and simpleRL‑reason—covering their architectures, reinforcement‑learning pipelines, and code implementations.
The article dissects NVCC’s compilation pipeline, showing how internal registration functions from host_runtime.h are injected into the host binary, how a simple CUDA demo is processed with --dryrun, and how the generated fatbin, PTX, and cubin files are linked and registered for GPU execution.
