chore: file refactor, imported zio

next steps are running the threads multithreaded  and measuring for
errors

build.sbt

@@ -1,7 +1,6 @@
 scalaVersion := "3.7.4"
 version := "1.0"
 name := "torque"
-organization := "com.nsrddyn"
 
 libraryDependencies += "dev.zio" %% "zio" % "2.1.22"
 libraryDependencies += "org.scalatest" %% "scalatest" % "3.2.19" % Test

src/Main.scala

@@ -11,18 +11,20 @@ enum Status:
   case FAIL 
 
 
-object Torque {
+object Torque extends ZIOAppDefault {
 
   println("hello world")
 
-  @main def main(args: String*): Unit =  {
+  @main def main(args: String*): Unit =  { println("\u001b[2J\u001b[H")
-    // ANSI ESCAPE CODE: clear screen
-    println("\u001b[2J\u001b[H")
     println("--- TORQUE STRESS TESTING UTILITY ---")
 
     var tester: CholeskyDecompositionTest = new CholeskyDecompositionTest
     println(tester.test())
 
   }
+
+  var p: Prime = new Prime
+  p.run()
+
 }
 

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
+  }
+}
+

src/Tests/Tests.scala

@@ -2,8 +2,17 @@ package com.nsrddyn.Tests
 
 import com.nsrddyn.fpu.CholeskyDecomposition
 import scala.collection.immutable.ListSet
+import zio._
 
-class CholeskyDecompositionTest extends CholeskyDecomposition {
+class TestsRunner extends ZIOAppDefault {
+  
+  def run =
+    println("Hello world")
+
+
+}
+
+class CholeskyDecompositionTest  {
 
   def test(): Unit = {
 

src/Traits/Workload.scala

@@ -1,7 +1,5 @@
 package com.nsrddyn.Traits
 
-import zio._
-
 trait Workload {
 
   def name: String

src/main/scala/com/nsrddyn/ALU/Hash.scala

@@ -1,31 +0,0 @@
-package com.nsrddyn.alu
-
-import scala.util.hashing
-
-class Hash {
-
-import scala.util.hashing.MurmurHash3
-
-  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) 
-
-  }
-}

src/main/scala/com/nsrddyn/ALU/Prime.scala

@@ -1,64 +0,0 @@
-package com.nsrddyn.alu
-import com.nsrddyn.alu.Prime
-import com.nsrddyn.tools.Benchmark
-import com.nsrddyn.test
-
-class Prime() extends  {
-
-  /*
-   * 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 extends Workload {
-
-  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)
-
-  } 
-}

src/main/scala/com/nsrddyn/FPU/CholeskyDecomposition.scala

@@ -1,46 +0,0 @@
-package com.nsrddyn.fpu
-
-import scala.math._
-import scala.collection.immutable.ListSet
-import scala.collection.mutable.ArrayBuffer
-
-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
-  }
-}
-

src/main/scala/com/nsrddyn/FPU/FPU.scala

@@ -1,6 +0,0 @@
-package com.nsrddyn.fpu
-
-
-class FPU {
-  
-}

src/main/scala/com/nsrddyn/FPU/Matrix.scala

@@ -1,5 +0,0 @@
-package com.nsrddyn.fpu
-
-class Matrix {
-  
-}