dougallj

# IDA (disassembler) and Hex-Rays (decompiler) plugin for Apple AMX
#
# WIP research. (This was edited to add more info after someone posted it to
# Hacker News. Click "Revisions" to see full changes.)
#
# Copyright (c) 2020 dougallj


# Based on Python port of VMX intrinsics plugin:
# Copyright (c) 2019 w4kfu - Synacktiv

planetceres

#!/usr/bin/env bash


# PCL
sudo apt install libpcl-dev


# VTK
# Ref: https://discourse.vtk.org/t/installing-vtk-in-ubuntu-18-04/2147/4
sudo apt install cmake \

moritzkuhne

# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
from IPython import get_ipython


# %%
# get_ipython().run_line_magic('load_ext', 'autoreload')
# get_ipython().run_line_magic('autoreload', '1')

lonsty

sudo apt-get update

# Dependency for pillow-simd on Ubuntu 18.04
sudo apt-get install -y --fix-missing \
    libjpeg-turbo8-dev \
    zlib1g-dev \
    libtiff5-dev \
    liblcms2-dev \
    libfreetype6-dev \
    libwebp-dev \

travisdowns

The counters that are the easiest to understand and the best for making ratios that are internally consistent (i.e., always fall in the range 0.0 to 1.0) are the mem_load_retired events, e.g., mem_load_retired.l1_hit and mem_load_retired.l1_miss.

These count at the instruction level, i.e., the universe of retired instructions. For example, could make a reasonable hit ratio from mem_load_retired.l1_hit / mem_inst_retired.all_loads and it will be sane (never indicate a hit rate more than 100%, for example).

That one isn't perfect though, in that it may not reflect the true costs of cache misses and the behavior of the program for at least the following reasons:

• It appplies only to loads and can't catch misses imposed by stores (AFAICT there is no event that counts store misses).
• It only counts loads that retire - a lot of the load activity in your process may be due to loads on a speculative path that never retire. Loads on a speculative path may bring in data that is never used, causing misses and d

FedeMiorelli

# -*- coding: utf-8 -*-
"""
Created on 2019-08-22 09:37:36

@author: fmiorell
"""

#  This script registers the "turbo" colormap to matplotlib, and the reversed version as "turbo_r"
#  Reference:  https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html

nikolaskaralis

% Look-up table for Turbo colormap.
% Includes example usage and comparison with other colormaps.
% Adapted for Matlab from https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f

% Nikolas Karalis, 21 August 2019

turbomap = [[0.18995,0.07176,0.23217];[0.19483,0.08339,0.26149];[0.19956,0.09498,0.29024];[0.20415,0.10652,0.31844];[0.20860,0.11802,0.34607];[0.21291,0.12947,0.37314];[0.21708,0.14087,0.39964];[0.22111,0.15223,0.42558];[0.22500,0.16354,0.45096];[0.22875,0.17481,0.47578];[0.23236,0.18603,0.50004];[0.23582,0.19720,0.52373];[0.23915,0.20833,0.54686];[0.24234,0.21941,0.56942];[0.24539,0.23044,0.59142];[0.24830,0.24143,0.61286];[0.25107,0.25237,0.63374];[0.25369,0.26327,0.65406];[0.25618,0.27412,0.67381];[0.25853,0.28492,0.69300];[0.26074,0.29568,0.71162];[0.26280,0.30639,0.72968];[0.26473,0.31706,0.74718];[0.26652,0.32768,0.76412];[0.26816,0.33825,0.78050];[0.26967,0.34878,0.79631];[0.27103,0.35926,0.81156];[0.27226,0.36970,0.82624];[0.27334,0.38008,0.84037];[0.27429,0.39043,0.85393];

YashasSamaga

#include <iostream>
#include <algorithm>
#include <fstream>
#include <vector>
#include <chrono>

#include <opencv2/dnn.hpp>
#include <opencv2/highgui.hpp>

#include "benchmark.hpp"

mikhailov-work

// Copyright 2019 Google LLC.
// SPDX-License-Identifier: Apache-2.0

// Polynomial approximation in GLSL for the Turbo colormap
// Original LUT: https://gist.github.com/mikhailov-work/ee72ba4191942acecc03fe6da94fc73f

// Authors:
//   Colormap Design: Anton Mikhailov ([email protected])
//   GLSL Approximation: Ruofei Du ([email protected])

mikhailov-work

# Copyright 2019 Google LLC.
# SPDX-License-Identifier: Apache-2.0

# Author: Anton Mikhailov

turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913