Marek Kolodziej's repositories
open-gpu-doc
Documentation of NVIDIA chip/hardware interfaces
awesome-reMarkable
A curated list of projects related to the reMarkable tablet
bazel-examples
Examples of Bazel use
bdf
Avnet Board Definition Files
brevitas
Brevitas: quantization-aware training in Pytorch
data-parallel-CPP
Source code for 'Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL' by James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian (Apress, 2020).
direwolf
Dire Wolf is a software "soundcard" AX.25 packet modem/TNC and APRS encoder/decoder. It can be used stand-alone to observe APRS traffic, as a tracker, digipeater, APRStt gateway, or Internet Gateway (IGate). For more information, look at the bottom 1/4 of this page and in https://github.com/wb2osz/direwolf/blob/dev/doc/README.md
Get_Moving_With_Alveo
For publishing the source for UG1352 "Get Moving with Alveo"
gradient-checkpointing
Make huge neural nets fit in memory
iree
👻
MinkowskiEngine
Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
nandland
All code found on nandland is here. underconstruction.gif
nvidia_libs_test
Tests and benchmarks for cudnn (and in the future, other nvidia libraries)
raytracinginoneweekendincuda
The code for the ebook Ray Tracing in One Weekend by Peter Shirley translated to CUDA by Roger Allen. This work is in the public domain.
rpi-gpio-dma-demo
Performance writing to GPIO with CPU and DMA on the Raspberry Pi
rules_cuda
Starlark implementation of bazel rules for CUDA.
rules_cuda_examples
This repo holds the extended examples for rules_cuda.
spconv
Spatial Sparse Convolution Library
TensorRT
TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.
torch2trt
An easy to use PyTorch to TensorRT converter
TransformerEngine
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference.
Vitis_Accel_Examples
Vitis_Accel_Examples