1 files changed, 0 insertions, 138 deletions
diff --git a/src/Ops/Prime.scala b/src/Ops/Prime.scala
deleted file mode 100644
index bd93ee1..0000000
--- a/src/Ops/Prime.scala
+++ /dev/null
@@ -1,138 +0,0 @@
-package com.nsrddyn.ops
-import com.nsrddyn.tools.Benchmark
-import scala.util.hashing
-import scala.util.hashing.MurmurHash3
-import  com.nsrddyn.Traits.*
-import scala.math._
-import scala.collection.immutable.ListSet
-import scala.collection.mutable.ArrayBuffer
-
-
-class Prime()  {
-
-  /*
-   * Calculate all primes up to limit
-   * This should stress the ALU in someway,
-   * doing this in a predictable manner, 
-   * will hopefully keep the cpu pipeline busy
-   * and that way stress the branch predictor
-   *
-   * math.sqrt(n) => a prime number has 2 factors, one of the factors 
-   * of the prime numbers has to be smaller then n 
-   * after that we check if the number is whole number and thereby checking if its a prime
-   *
-   */
-
-
-  /*
-   * TODO: I did the countrary of what i wanted to accieve with the is prime function
-   *       We want the function to be less optimized so that the CPU has more work == more stress 
-   */
-
-
-  def isPrime(n: Int): Boolean = {
-    if n <= 1 then false
-    else !(2 to math.sqrt(n).toInt).exists(i => n % i  == 0)
-
-
-  }
-
-  def run(n: Int, result: Boolean): Unit = {
-
-    for i <- 0 to n do if isPrime(i) == result then println("true") else println("false") 
-  }
-
-
-}
-
-
-
-
-class PrimeRunner {
-
-
-  def run(threads: Int): Unit = {
-
-    val pr = new Prime()
-    val br = new Benchmark()
-
-    /*
-     * test cases
-     *
-     * 7919 true
-     * 2147483647 false
-     */
-
-    val time = pr.run(7919, true)
-    println(time)
-
-  } 
-}
-
-
-class Hash {
-
-  def run(word: String, loopSize: Int): Unit = {
-
-    /* TODO: implement ALU friendly, so high speed hashing
-     * to continuously loop over voor stressing 
-     * ALU 
-     *
-     * While looking for hashing algorithmes to implement I stumbled on: 
-     * https://scala-lang.org/api/3.x/scala/util/hashing/MurmurHash3$.html
-     *
-     * which is an implemntation of **smasher** http://github.com/aappleby/smhasher
-     * the exact type of hashing algorithm I was looking for
-     *
-     * In the scala description they state: "This algorithm is designed to generate
-     * well-distributed non-cryptographic hashes. It is designed to hash data in 32 bit chunks (ints). "
-     * 
-     * (ints) -> ALU
-     *
-     */ 
-
-     for i <- 0 to loopSize do MurmurHash3.stringHash(word) 
-
-  }
-}
-
-class CholeskyDecomposition {
-
-  /*
-   *  Floating point operation to stress the cpu
-   *  Calculate the number of KFLOPS / FLOPS
-   *  implementation of the Cholesky decomposition
-   *  More information on the Cholesky decomposition at: 
-   *  https://en.wikipedia.org/wiki/Cholesky_decomposition
-   *  
-   *  Linpack uses the cholesky decomposition
-   *  https://www.netlib.org/linpack/
-   *
-   *  https://www.geeksforgeeks.org/dsa/cholesky-decomposition-matrix-decomposition/
-   *
-   *  The Cholesky decomposition maps matrix A into the product of A = L · LH where L is the lower triangular matrix and LH is the transposed,
-   *  complex conjugate or Hermitian, and therefore of upper triangular form (Fig. 13.6).
-   *  This is true because of the special case of A being a square, conjugate symmetric matrix. 
-   */ 
-
-  def run(matrix: Vector[Vector[Int]]): Unit = {
-
-    val size: Int = matrix.size
-    val lower: ArrayBuffer[ArrayBuffer[Int]] = ArrayBuffer[ArrayBuffer[Int]]()
-
-    for 
-      i <- 0 to size 
-      j <- 0 until i 
-    do 
-      if i == j then lower(i)(j) = getSquaredSummation(lower, i, j, matrix) else lower(j)(j) = getReversedSummation(lower, i, j, matrix)
-
-  }
-
-  private def getReversedSummation(lower: ArrayBuffer[ArrayBuffer[Int]], i: Int, j: Int, matrix: Vector[Vector[Int]]) = {
-    math.sqrt(matrix(j)(j) - (0 until j).map { k => lower(i)(k) * lower(j)(k) }.sum).toInt
-  }
-  private def getSquaredSummation(lower: ArrayBuffer[ArrayBuffer[Int]], i: Int, j: Int, matrix:  Vector[Vector[Int]]) = {
-    ((matrix(i)(j) - (0 until j).map { k => math.pow(lower(j)(k), 2)}.sum) / lower(j)(j)).toInt
-  }
-}
-