1 files changed, 138 insertions, 0 deletions
diff --git a/src/Ops/Prime.scala b/src/Ops/Prime.scala
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--- /dev/null
+++ b/src/Ops/Prime.scala
@@ -0,0 +1,138 @@
+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
+  }
+}
+