corporatepiyush

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

corporatepiyush

import java.util.Random;

public class For {

  static final int SIZE = 1_000_000;

  public static void main(String[] args) {
    int[] data = new int[SIZE];
    Random rnd = new Random(42);
    for (int i = 0; i < SIZE; i++) data[i] = rnd.nextInt(1000) - 500;

corporatepiyush

#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h> 
// REQUIRED for the M1/Apple Silicon native BLAS implementation (Accelerate Framework)
// gcc -O3 -Wall -Wextra Matmul.c -o Matmul -lm -framework Accelerate

#include "cblas.h" 

// --- Utility Functions ---

corporatepiyush

echo 'export DEB_CFLAGS_MAINT_APPEND="-O3 -march=native -ftree-vectorize -flto -fprefetch-loop-arrays"' | sudo tee -a /etc/dpkg/buildflags.conf
echo 'export DEB_CXXFLAGS_MAINT_APPEND="-O3 -march=native -ftree-vectorize -flto -fprefetch-loop-arrays"' | sudo tee -a /etc/dpkg/buildflags.conf

corporatepiyush

/// @file A high-performance buffer pool implementation in Dart for managing TypedData.
/// @author Piyush Katariya
/// @version 1.5.0
library buffer_pool;

import 'dart:typed_data';
import 'dart:math';
import 'dart:ffi';
import 'package:ffi/ffi.dart';

corporatepiyush

---

## Memory Management & Allocation

1. Pre-allocate Slices with Known Capacity When the eventual size of a slice is known, pre-allocating with make([]T, 0, capacity) creates the underlying array a single time. This critical practice avoids multiple, inefficient reallocations and the expensive process of copying all existing elements to a new, larger array as you append data.

2. Use the arena Package for Short-Lived Objects New in recent Go versions, the arena package provides a safe way to allocate memory that can be freed all at once. This is perfect for functions that create many temporary objects (like during a single request), as it can nearly eliminate GC pressure from that workload.

corporatepiyush

// benchmark.js
import { faker } from '@faker-js/faker';
import fs from 'fs';
import crypto from 'crypto';

// Text encoder/decoder for binary operations
const textEncoder = new TextEncoder();
const textDecoder = new TextDecoder();

// Mock MessageType enum

corporatepiyush

#define _GNU_SOURCE
#include "bufferpool.h"
#include <stdlib.h>
#include <string.h>
#include <pthread.h>
#include <stdatomic.h>
#include <dlfcn.h>
#include <errno.h>
#include <sys/mman.h>
#include <inttypes.h>

corporatepiyush

A Language-Agnostic Guide to High-Performance Software Refactoring

1. CPU Optimization: From Microarchitecture to Application Logic

1.1. Low-Level CPU and Cache Optimization

1.1.1. Understanding CPU-Bound vs. I/O-Bound Workloads

corporatepiyush

'use strict';
process.title = 'http2server';

console.error('SERVER PID', process.pid);

/* ---------- Linux early tuning ------------------------------ */

if (process.platform === 'linux') {
  try {
    require('fs').writeFileSync('/proc/sys/net/core/somaxconn', '65535');