abelardojarab

Parallel Computation: Task Execution, ProcessPoolExecutor, and Ray for Matrix Multiplication

Introduction

Parallel execution is essential for optimizing computational workloads such as matrix multiplication, large-scale data processing, and distributed computing. In this document, we explore:

1. Creating a Generalized Task Execution Framework
2. Using ProcessPoolExecutor and Ray for 1D and 2D Task Execution
3. Parallel Matrix Multiplication (Stripe-Based vs. Block-Based Approaches)

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abelardojarab

Complete Guide to Parallel Processing in Python

Table of Contents

1. Queue Types
2. ProcessPoolExecutor: Map and Submit
3. Matrix Operations
4. MapReduce Implementation
5. Roofline Model Analysis
6. Function Event Tracking
7. Ray for Distributed Computing

abelardojarab

import numpy as np
import multiprocessing
from concurrent.futures import ProcessPoolExecutor

def process_chunk(chunk, chunk_start_idx):
    """Processes a chunk of samples and tracks function lifetimes."""
    active_functions = {}  # {function_name: start_time}
    events = []

    for offset, functions in enumerate(chunk):

abelardojarab

#include <iostream>
#include <unordered_map>
#include <vector>
#include <queue>
#include <stack>
#include <unordered_set>

class Graph {
private:
  std::unordered_map<int, std::vector<int>> adjList;

abelardojarab

/******************************************************************************

                              Online C++ Compiler.
               Code, Compile, Run and Debug C++ program online.
Write your code in this editor and press "Run" button to compile and execute it.

*******************************************************************************/

#include <iostream>
#include <atomic>

abelardojarab

/******************************************************************************

                              Online C++ Compiler.
               Code, Compile, Run and Debug C++ program online.
Write your code in this editor and press "Run" button to compile and execute it.

*******************************************************************************/

#include <iostream>
#include <vector>

abelardojarab

#include <iostream>
#include <memory>
#include <regex>
#include <cassert>
#include <experimental/filesystem>

#include <fcntl.h>
#include <sys/mman.h>
#include <sys/stat.h>
#include <sys/types.h>

abelardojarab

@startuml
start

:Request created;

while (Review request) is (accepted)
     :Jira ticket is created;
     :Assign Cloud Performance Engineer (PE) to Jira ticket;
     :CPE designs test plan;
     :CPE calculate CSP cost;

abelardojarab

Ubuntu Grub Boot Kernel

Boot a specific installed Ubuntu kernel using grub-reboot and grub-set-default.

This allows you to pick what kernel you want to boot on next reboot, or set the default, without having to know much about how grub works or editing config files.

Usage

  Usage: boot-kernel [options] [kernel]

call grub-reboot or grub-set-default to boot the provided kernel.

abelardojarab

From 6cf513dc8c5ab758072d894f10a58fbb4a146bd6 Mon Sep 17 00:00:00 2001
From: Jon Nettleton <[email protected]>
Date: Wed, 6 Oct 2021 09:16:35 -0400
Subject: [PATCH] Aarch64: Make memcpy more compatible with device memory

For normal non-cacheable memory ACE supports 4x128 bit r/w WRAP
transfers or 1x128 bit r/w INCR transfers.  By re-ordering the
stp's in memcpy / memmove we can accomodate this better without
impacting the existing code.