If you have spent any time deploying machine learning models outside of a data center, you have probably run into a frustrating reality. The tools that work beautifully on a cloud GPU server can be surprisingly awkward on a developer laptop or an edge device sitting in a factory. Two names come up constantly in … Read more
If you have been writing GPU kernels in Triton, you already know that performance is not just about writing correct code. It is about writing code that runs fast on the actual hardware. One of the most powerful tools Triton gives you for this is its built-in autotuning system. Triton autotune lets you define multiple … Read more
Choosing the right development environment is one of the most important early decisions you will make as a new JavaScript learner. In 2026, the ecosystem of coding tools is more powerful, intelligent, and beginner-friendly than ever before. From lightweight editors that load instantly to fully featured IDEs with built-in debugging, Git integration, and AI-powered code … Read more
If you are just getting started with GPU programming, you have probably heard the names Nsight Compute and Nsight Systems thrown around in forums, documentation, and tutorials. Both tools come from NVIDIA and both are used for profiling GPU workloads, but they serve very different purposes. Understanding the difference between Nsight Compute vs Nsight Systems … Read more
If you are running machine learning workloads on a Mac with Apple Silicon, you have probably come across two terms that sound similar but serve different purposes: MPS kernels and MPSGraph. Both are part of Apple’s Metal Performance Shaders framework, both run on the GPU, and both are deeply involved in how frameworks like PyTorch … Read more
If you own a Mac with an M-series chip and want to train or run machine learning models locally, the Apple Silicon PyTorch MPS backend is the feature you need to understand. Introduced with PyTorch 1.12, the Metal Performance Shaders (MPS) backend lets your Mac GPU accelerate deep learning workloads directly, without needing a cloud … Read more
GPU computing is no longer the exclusive territory of CUDA or OpenCL. With the arrival of wgpu, Rust developers now have a safe, cross-platform, and portable way to run massively parallel workloads on the GPU. Whether you are targeting Vulkan on Linux, Metal on macOS, DirectX 12 on Windows, or even WebAssembly in a browser, … Read more
For years, Windows users with AMD graphics cards faced a frustrating wall when trying to do serious machine learning work. NVIDIA’s CUDA ecosystem had long enjoyed native support under Windows Subsystem for Linux 2 (WSL2), while AMD’s ROCm platform remained a Linux-only affair. That gap has been closing steadily. With ROCm 6.1.3, AMD officially declared … Read more
Running deep learning workloads on AMD GPUs has become significantly more practical over the last couple of years. ROCm, which stands for Radeon Open Compute, is AMD’s open-source software platform that allows PyTorch and other frameworks to offload computation to AMD graphics hardware much like CUDA does for NVIDIA cards. Ubuntu 24.04 LTS is now … Read more
The GPU computing landscape has never been more competitive. As NVIDIA’s CUDA continues to dominate AI and HPC workloads, developers building truly cross-platform applications face an important three-way decision: SYCL, OpenCL, or Vulkan Compute. Each of these open, vendor-neutral APIs offers a distinct trade-off between abstraction, performance, portability, and ecosystem maturity. Choosing the wrong one … Read more