TrentBrick

""" 
PyTorch has pack_padded_sequence this doesn’t work with dense layers. For sequence data with high variance in its length 
the best way to minimize padding and masking within a batch is by feeding in data that is already grouped by sequence length 
(while still shuffling it somewhat). Here is my current solution in numpy. 
I will need to convert every function over to torch to allow it to run on the GPU and am sure there are many other 
ways to optimize it further. Hope this helps others and that maybe it can become a new PyTorch Batch Sampler someday.

General approach to how it works:

Decide what your bucket boundaries for the data are.

silvesthu

GPU Optimization for GameDev

Graphics Pipeline / GPU Architecture Overview

• 2011 - A trip through the Graphics Pipeline 2011
• 2013 - Performance Optimization Guidelines and the GPU Architecture behind them
• 2015 - Life of a triangle - NVIDIA's logical pipeline
• 2015 - Render Hell 2.0
• 2016 - How bad are small triangles on GPU and why?
• 2017 - GPU Performance for Game Artists
• 2019 - Understanding the anatomy of GPUs using Pokémon

zishun

# OpenMesh-Python Cheat Sheet
'''
documentation: https://openmesh-python.readthedocs.io/en/latest/
OpenMesh-Python official repository:
  https://www.graphics.rwth-aachen.de:9000/OpenMesh/openmesh-python
C++ documentation:
  https://www.openmesh.org/media/Documentations/OpenMesh-Doc-Latest/a04099.html

Installation:
* pip install openmesh  # https://pypi.org/project/openmesh/

benawad

" Specify a directory for plugins
call plug#begin('~/.vim/plugged')

Plug 'neoclide/coc.nvim', {'branch': 'release'}
Plug 'scrooloose/nerdtree'
"Plug 'tsony-tsonev/nerdtree-git-plugin'
Plug 'Xuyuanp/nerdtree-git-plugin'
Plug 'tiagofumo/vim-nerdtree-syntax-highlight'
Plug 'ryanoasis/vim-devicons'
Plug 'airblade/vim-gitgutter'

coolreader18

class E(BaseException):
    def __new__(cls, *args, **kwargs):
        return cls
def a(): yield
a().throw(E)

swatson555

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <assert.h>

// We'll have 128 tokens. Each token can be up to 32 characters long.
char token[128][32];

int lexer(char* input) {

bjourne

// Compile and run with:
//
//   gcc -O3 -march=native fast-strlen.c -lpthread -o fast-strlen
//       && ./fast-strlen
//
// Use gcc because clang is too smart and optimizes away parts of the
// benchmark. Results on Xeon(R) CPU E5-2650 v4 @ 2.20GHz with gcc
// 9.4.0:
//
//   Scanning 10 times over 4.00GB...

crisdosaygo

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <openssl/sha.h>

#define TARGET_PREFIX "20250327"
#define MAX_WORDS 256
#define MAX_TEXT 2048
#define MAX_ATTEMPTS (1ULL << 32) // 2^32 attempts (~4.3B, enough for 8-char prefix)