#cuda

SVShivam Vishwakarmashivam-vishwakarma.hashnode.dev

From Hash Rates to AI Compute: Repurposing Crypto Mining for Machine Learning in 2025

The digital gold rush of cryptocurrency mining has seen its peaks and valleys. If you’ve been in the crypto space, you’ve likely witnessed the enormous investments poured into powerful GPU farms, all dedicated to solving complex cryptographic puzzles...

Feb 77 min read

WKWahid Khanwahidk.hashnode.dev

Ghosts in the Mandelbrot Set

$$z_n = z_{n-1}^2 + c$$This one simple equation is responsible for rendering some very interesting visuals. What is this equation really? One way to think about the equation is as a complex number generator. You pick an initial seed point c and then ...

Dec 9, 20257 min read

MFMd. Fahim Bin Aminfreecodecamp.org

How to Set Up CUDA and WSL2 for Windows 11 (including PyTorch and TensorFlow GPU)

If you’re working on complex Machine Learning projects, you’ll need a good Graphics Processing Unit (or GPU) to power everything. And Nvidia is a popular option these days, as it has great compatibility and widespread support. If you’re new to Machin...

Dec 3, 202518 min read

VSVarada Santoshvvnasantosh.hashnode.dev

Optimizing GEMM: GPU Architecture Essentials

Every time you ask ChatGPT a question, get a movie recommendation on Netflix, or watch your phone recognize faces in photos, billions of matrix multiplications are happening behind the scenes. This fundamental mathematical operation has become the co...

Nov 27, 202522 min read

VSVarada Santoshvvnasantosh.hashnode.dev

Understanding CUDA GEMM: Foundations for Optimization

In our previous blog, we explored GPU computing fundamentals: memory hierarchies, thread organization, warps, memory coalescing, and kernel classification (memory-bound vs. compute-bound). In this blog, we apply these concepts to optimize GEMM (Gener...

Nov 27, 202543 min read

ARAnton R Gordonantonrgordon.hashnode.dev

Optimizing CUDA and RAG Systems with Profiling and Evaluation Frameworks

By Anton R Gordon Understanding both hardware efficiency and answer quality is essential for building high-performance, trustworthy AI systems. CUDA workloads rely heavily on GPU utilization and kernel design, while retrieval-augmented generation (RA...

Nov 24, 20253 min read

SPSagar Parmargpu-operator-in-kubernetes.hashnode.dev

NVIDIA GPU Operator Explained: Simplifying GPU Workloads on Kubernetes

Introduction While GPUs have long been a staple in industries like gaming, video editing, CAD, and 3D rendering, their role has evolved dramatically over the years. Originally designed to handle graphics-intensive tasks, GPUs have proven to be powerf...

Nov 5, 202511 min read

YOYuki Ohirablinkfast.hashnode.dev

Under-60ms End-to-End RealTime Remote Desktop on Windows — NVENC/CUDA/FEC

TL:DR Clam: On a wired LAN with server and client on separate PCs, disabling all-intra (IDR every frame) and using low-latency IP (B=0) delivers p50 53 ms, p95 71 ms, p99 93 ms end-to-end.Most frames sit in the 50–80 ms band; exceedance is low (\>80 ...

Nov 4, 20259 min read

MIMichael Ivertowskimivertowski.hashnode.dev

CUDA Kernel Execution Debugging Journey

Short version: we went from 8/70 passing CUDA tests to a stable, auditable path by fixing NVRTC name resolution, argument marshaling, and unified-memory sync in DotCompute. No mysticism—just careful pointers and fewer foot-guns. TL;DR NVRTC will ha...

Sep 4, 20254 min read

PBPierre Ballifpbhnblog.ballif.eu

CUDA for Wave Simulation - Part 3: Efficient CUDA

Non-trivial CUDA The current snippet of code calling CUDA is: cuda_step<<< 1, 1 >>> This uses only one CUDA thread, and is probably extremely inefficient. To verify this, let’s add some way to time the program. I could use NVIDIA’s nsys, or just the...

Aug 31, 20258 min read

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Tag feed

#cuda

From Hash Rates to AI Compute: Repurposing Crypto Mining for Machine Learning in 2025

Ghosts in the Mandelbrot Set

How to Set Up CUDA and WSL2 for Windows 11 (including PyTorch and TensorFlow GPU)

Optimizing GEMM: GPU Architecture Essentials

Understanding CUDA GEMM: Foundations for Optimization

Optimizing CUDA and RAG Systems with Profiling and Evaluation Frameworks

NVIDIA GPU Operator Explained: Simplifying GPU Workloads on Kubernetes

Under-60ms End-to-End RealTime Remote Desktop on Windows — NVENC/CUDA/FEC

CUDA Kernel Execution Debugging Journey

CUDA for Wave Simulation - Part 3: Efficient CUDA

#cuda

Search Hashnode

#cuda

From Hash Rates to AI Compute: Repurposing Crypto Mining for Machine Learning in 2025

Ghosts in the Mandelbrot Set

How to Set Up CUDA and WSL2 for Windows 11 (including PyTorch and TensorFlow GPU)

Optimizing GEMM: GPU Architecture Essentials

Understanding CUDA GEMM: Foundations for Optimization

Optimizing CUDA and RAG Systems with Profiling and Evaluation Frameworks

NVIDIA GPU Operator Explained: Simplifying GPU Workloads on Kubernetes

Under-60ms End-to-End RealTime Remote Desktop on Windows — NVENC/CUDA/FEC

CUDA Kernel Execution Debugging Journey

CUDA for Wave Simulation - Part 3: Efficient CUDA