Douwe den Blanken V0XNIHILI

AWS EC2 LaTeX

Setup

[ec2-user ~]$ wget http://ftp.jaist.ac.jp/pub/CTAN/systems/texlive/tlnet/install-tl-unx.tar.gz
[ec2-user ~]$ tar xzf install-tl-unx.tar.gz
[ec2-user ~]$ cd install-tl-2017xxxx
[ec2-user ~]$ sudo ./install-tl

Multicore Python

Below you can find some sample template code which you can use to run multicore Python computations.

# =================================================================================================
# Imports
# =================================================================================================

import multiprocessing as mp

On my 2014 MacBook Pro (2.5 GHz), this vectorized version is about 250 times faster when used for a large numbers of triangles compared to the original algorithm. You can also parallelize this code over multiple threads, but due to the large overhead of creating a thread pool, this is only really useful when you have a very large number of rays.

I also tried to vectorize the rays, instead of using a loop. I did this by using:

np.reshape(np.array(np.meshgrid(np.arange(0, len(ray_direction), 1), np.arange(0, len(triangles_vertices), 1))), (2, resulting_array_length))

to create all combinations of indices of the rays and the triangles. However, using this process, combined with using np.take() to actually create these combinations is about twice as slow as the code below.

	for y in range(0, 1000):
	for m in range(1,13):
	max_day = 32

	# Month length correction
	if m == 11 or m == 9 or m == 6 or m == 4:
	max_day = 31
	# Leap year correction
	elif m == 2:
	y_actual = y + 2000

	void divide (float a, float b, float *c)
	{
	#pragma HLS interface s_axilite port=a
	#pragma HLS interface s_axilite port=b
	#pragma HLS interface s_axilite port=c
	#pragma HLS interface ap_ctrl_none port=return
	*c = a / b;
	}

	from typing import List, Any, Callable

	def zip_with (func: Callable[[Any, Any], Any], list1: List[Any], list2: List[Any]) -> Any:
	"""
	>>> zip_with (lambda x, y: x + y, [1, 2, 3], [4, 5, 6])
	[5, 7, 9]
	"""

	return [func (x, y) for x, y in zip (list1, list2)]

	import torch
	import torch.nn as nn
	import torchvision
	from torch.utils.data import Dataset

	class SequentialMNIST(Dataset):

	def __init__(self, MNIST_dataset: torchvision.datasets.MNIST):
	self.MNIST_dataset = MNIST_dataset

	"""Data taken from paper: Computing's Energy Problem (and what we can do about it) - Mark Horowitz

	All output energies are in pJ (pico Joule)
	"""

	import math

	E_INT_ADD_PER_BIT = (0.03 / 8 + 0.1 / 32) / 2

	sysctl -n machdep.cpu.brand_string

	# Example output: Intel(R) Core(TM) i7-4870HQ CPU @ 2.50GHz