C

cuda

Template Numerical Library https://tnl-project.org/

A low-overhead software methodology for memory isolation of kernels in multi-tenant General-Purpose GPU environments.

Building different Python base docker image with and without CUDA

Bandicoot: user-friendly C++ library for GPU accelerated linear algebra, integrating with CUDA and OpenCL

High-performance cryptographic suite featuring massive parallel execution of Secp256k1, SHA-256, and RIPEMD-160 using NVIDIA CUDA (GPU) and AVX2 (CPU). Includes a standalone CashAddr library.

KeyKiller is designed to achieve extreme performance on modern NVIDIA GPUs, solving the Satoshi puzzle. It leverages CUDA, warp-level parallelism, and batched EC operations to push the boundaries of cryptographic key discovery. It is commonly used in research projects such as Secp256k1-CUDA,Brainflayer,BitCrack, Keyhunt CUDA BitCrack,RCKangaroo.

Electromagnetic Simulation Using the FDTD Method with Python

Bitcoin Mini Key Hunter is a tool that focuses on searching for 22-digit private keys starting with S and performing cracking drills. It uses the powerful GPU parallel computing capabilities of CUDA technology, similar to research tools such as brainflayer,keyhunt,BitCrack, ecloop,keyhunt cuda,etc.

After optimization, version 3080 is equivalent to version 4090. I fixed address matching errors and private key conversion errors, modified the random generator, added puzzles suitable for specific regions, and more. It is commonly used in puzzle games, Brainflayer, Keyhunt, BitCrack, VanitySearch, etc.

Set of Feature Selection Tools that make use of parallel environments through CUDA, MPI and Threads

Examples of Cuda By Example book written by me

SuperConga: Superconductivity simulation framework on GPU.

An open source library of GPU Programming Tutorials in the context of Particle Physics.

ElasWave3D is a novel 3D solver optimized for simulating seismic waves in terrains with complex topography. This solver leverages GPU acceleration to enhance performance.

CUDA-accelerated implementation of Partial 3D Discrete Wavelet Transform, enabling efficient multi-resolution analysis of volumetric data. Designed optimized GPU kernels to support partial inverse transform, allowing reconstruction of arbitrary points in a 3D signal without processing the entire dataset.

Some basic CUDA/OpenAcc examples in Fortran, plus a few utilities