finished lab 5

.vscode/settings.json

@@ -59,6 +59,7 @@
         "xutility": "cpp",
         "stop_token": "cpp",
         "thread": "cpp",
-        "array": "cpp"
+        "array": "cpp",
+        "mutex": "cpp"
     }
 }

lab05/matrixVectorProductRowCalculation.cpp

@@ -0,0 +1,134 @@
+#include <benchmark/benchmark.h>
+#include <cmath>
+#include <iostream>
+#include <thread>
+#include "matrix.h"
+#include "test.h"
+#include <fstream>
+#include <string>
+
+// Create the matrix and vector to be multiplied and fill them
+// with some sensible initial values.
+std::pair<Matrix, std::vector<double>> createMatrixAndVector()
+{
+    const int n = 1e3 * 9;
+    Matrix mat(n, n);
+    for (int i = 0; i < n; ++i)
+    {
+        for (int j = 0; j < n; ++j)
+        {
+            mat(i, j) = pow(-1, i) * (i + j);
+        }
+    }
+
+    std::vector<double> vec(n);
+    for (int i = 0; i < n; ++i)
+    {
+        vec[i] = 1. / (i + 1);
+    }
+
+    return std::pair(mat, vec);
+}
+
+// Verify that the computed result is correct. Rather inefficient,
+// since it runs on a single core.
+void verifyResult(const std::vector<double> result)
+{
+    auto [mat, vec] = createMatrixAndVector();
+    const int n = vec.size();
+
+    for (int i = 0; i < n; ++i)
+    {
+        double expected = 0;
+        for (int j = 0; j < n; ++j)
+        {
+            expected += mat(i, j) * vec[j];
+        }
+        check(result[i], expected);
+    }
+}
+
+void computeResult(
+    const Matrix &mat,
+    const std::vector<double> &vec,
+    std::vector<double> &result,
+    int row)
+{
+    int n = vec.size();
+    for (int col = 0; col < n; col++)
+    {
+        result[row] += mat(row, col) * vec[col];
+    }
+}
+
+void workerThread(
+    const Matrix &mat,
+    const std::vector<double> &vec,
+    std::vector<double> &result,
+    std::atomic<int> &rowIndex,
+    int &rowsComputed)
+{
+    while (true)
+    {
+        int row = rowIndex.fetch_add(1);
+        if (row >= vec.size()) break;
+        computeResult(mat, vec, result, row);
+        rowsComputed++;
+    }
+}
+
+void storeRowsComputedPerThread(std::vector<int> &rowsComputedPerThread, int threadCount, int stateCount) {
+    std::stringstream fileName;
+    fileName << "stats" << threadCount << "_" << stateCount << ".csv";
+    std::ofstream MyFile(fileName.str());
+    for (auto entry : rowsComputedPerThread) {
+      MyFile << entry << ",";
+    }
+    MyFile.close();
+}
+
+void benchmarkComputeResult(benchmark::State &state)
+{
+    int threadCount = state.range(0);
+    auto [mat, vec] = createMatrixAndVector();
+    int stateCount = 0;
+    for (auto _ : state)
+    {
+        std::atomic<int> rowIndex = 0;
+        std::vector<std::thread> threadPool;
+        std::vector<double> result(vec.size(), 0);
+        std::vector<int> rowsComputedPerThread(threadCount, 0);
+        for (int threadIndex = 0; threadIndex < threadCount; threadIndex++)
+        {
+            threadPool.emplace_back(
+                workerThread, std::ref(mat), std::ref(vec),
+                std::ref(result), std::ref(rowIndex), std::ref(rowsComputedPerThread[threadIndex]));
+        }
+
+        for (int threadIndex = 0; threadIndex < threadCount; threadIndex++)
+        {
+            threadPool[threadIndex].join();
+        }
+        storeRowsComputedPerThread(rowsComputedPerThread, threadCount, stateCount);
+        benchmark::DoNotOptimize(result);
+        stateCount++;
+    }
+}
+
+int main(int argc, char **argv)
+{
+    ::benchmark::Initialize(&argc, argv);
+
+    std::vector<int> threads = {1, 2, 3, 4, 6, 9, 12};
+
+    for (int i = 0; i < threads.size(); i++)
+    {
+        benchmark::RegisterBenchmark("idk", benchmarkComputeResult)
+            ->Arg(threads[i])
+            ->Unit(benchmark::kMillisecond);
+    }
+
+    ::benchmark::RunSpecifiedBenchmarks();
+
+    return 0;
+}

lab05/plot_mVPDI.py

@@ -0,0 +1,37 @@
+import json
+import matplotlib.pyplot as plt
+
+# Path to the benchmark results JSON
+filename = "./results_mVPRC.json"
+
+# Load the data
+with open(filename, "r") as f:
+    data = json.load(f)
+
+# Constants
+total_flops = 18000
+
+# Data storage
+thread_counts = []
+flops = []
+
+# Extract relevant data
+for entry in data["benchmarks"]:
+    thread_count = int(entry["name"].split("/")[-1])
+    time_per_iter_ms = entry["real_time"]
+    time_total_s = (time_per_iter_ms) / 1000  # convert ms to s
+
+    flops_per_second = total_flops / time_total_s
+
+    thread_counts.append(thread_count)
+    flops.append(flops_per_second)
+
+# Plotting
+plt.figure(figsize=(8, 5))
+plt.plot(thread_counts, flops, marker="o")
+plt.xlabel("Thread Count")
+plt.ylabel("Performance (FLOPS)")
+plt.title("Performance (FLOPS) vs Thread Count")
+plt.grid(True)
+plt.tight_layout()
+plt.show()

lab05/results_mVPDI.json

@@ -0,0 +1,124 @@
+{
+  "context": {
+    "date": "2025-04-19T12:35:39+02:00",
+    "host_name": "hpcvl1",
+    "executable": "./mVPDI",
+    "num_cpus": 12,
+    "mhz_per_cpu": 3100,
+    "cpu_scaling_enabled": true,
+    "caches": [
+      {
+        "type": "Data",
+        "level": 1,
+        "size": 32768,
+        "num_sharing": 1
+      },
+      {
+        "type": "Instruction",
+        "level": 1,
+        "size": 32768,
+        "num_sharing": 1
+      },
+      {
+        "type": "Unified",
+        "level": 2,
+        "size": 262144,
+        "num_sharing": 1
+      },
+      {
+        "type": "Unified",
+        "level": 3,
+        "size": 15728640,
+        "num_sharing": 6
+      }
+    ],
+    "load_avg": [0,0,0],
+    "library_build_type": "release"
+  },
+  "benchmarks": [
+    {
+      "name": "idk/1",
+      "run_name": "idk/1",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 9.0830782328266650e+01,
+      "cpu_time": 1.2582504000000050e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/2",
+      "run_name": "idk/2",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 4.8641373829450458e+01,
+      "cpu_time": 1.4497118999999614e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/3",
+      "run_name": "idk/3",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 3.4412161840591580e+01,
+      "cpu_time": 2.0588515000000029e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/4",
+      "run_name": "idk/4",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 2.7137287650257349e+01,
+      "cpu_time": 2.4066663999999349e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/6",
+      "run_name": "idk/6",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 1000,
+      "real_time": 2.0151479526888579e+01,
+      "cpu_time": 3.9556471800000281e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/9",
+      "run_name": "idk/9",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 860,
+      "real_time": 2.2973610189483438e+01,
+      "cpu_time": 7.7354559418604130e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/12",
+      "run_name": "idk/12",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 407,
+      "real_time": 2.0264966373334133e+01,
+      "cpu_time": 1.6165737911547986e+00,
+      "time_unit": "ms"
+    }
+  ]
+}

lab05/results_mVPRC.json

@@ -0,0 +1,124 @@
+{
+  "context": {
+    "date": "2025-04-19T13:46:32+02:00",
+    "host_name": "hpcvl1",
+    "executable": "./mVPRC",
+    "num_cpus": 12,
+    "mhz_per_cpu": 3100,
+    "cpu_scaling_enabled": true,
+    "caches": [
+      {
+        "type": "Data",
+        "level": 1,
+        "size": 32768,
+        "num_sharing": 1
+      },
+      {
+        "type": "Instruction",
+        "level": 1,
+        "size": 32768,
+        "num_sharing": 1
+      },
+      {
+        "type": "Unified",
+        "level": 2,
+        "size": 262144,
+        "num_sharing": 1
+      },
+      {
+        "type": "Unified",
+        "level": 3,
+        "size": 15728640,
+        "num_sharing": 6
+      }
+    ],
+    "load_avg": [0.23,0.93,0.51],
+    "library_build_type": "release"
+  },
+  "benchmarks": [
+    {
+      "name": "idk/1",
+      "run_name": "idk/1",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 8.6220469688996673e+01,
+      "cpu_time": 4.3488812999999737e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/2",
+      "run_name": "idk/2",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 4.8378378117922693e+01,
+      "cpu_time": 3.8731846000001013e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/3",
+      "run_name": "idk/3",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 3.5638073959853500e+01,
+      "cpu_time": 3.2923404000001710e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/4",
+      "run_name": "idk/4",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 100,
+      "real_time": 2.8666623858734965e+01,
+      "cpu_time": 3.4960458999997002e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/6",
+      "run_name": "idk/6",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 1379,
+      "real_time": 2.3543635421406215e+01,
+      "cpu_time": 5.5841710804931322e-01,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/9",
+      "run_name": "idk/9",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 734,
+      "real_time": 1.9770340910257534e+01,
+      "cpu_time": 1.0098632833787524e+00,
+      "time_unit": "ms"
+    },
+    {
+      "name": "idk/12",
+      "run_name": "idk/12",
+      "run_type": "iteration",
+      "repetitions": 0,
+      "repetition_index": 0,
+      "threads": 1,
+      "iterations": 419,
+      "real_time": 1.9854501259862168e+01,
+      "cpu_time": 1.6557223937947447e+00,
+      "time_unit": "ms"
+    }
+  ]
+}

lab05/svals/stats12_0.csv

@@ -0,0 +1 @@
+1231,1236,1130,1131,910,1158,730,99,102,312,394,567,

lab05/svals/stats12_1.csv

@@ -0,0 +1 @@
+1208,963,1189,1188,940,937,245,247,0,954,895,234,

lab05/svals/stats12_10.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_100.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_101.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_102.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_103.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_104.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_105.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_106.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_107.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_108.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_109.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_11.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_110.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_111.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_112.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_113.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_114.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_115.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_116.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_117.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_118.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_119.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_12.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_120.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_121.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_122.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_123.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_124.csv

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lab05/svals/stats12_125.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_126.csv

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lab05/svals/stats12_127.csv

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lab05/svals/stats12_128.csv

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lab05/svals/stats12_129.csv

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lab05/svals/stats12_13.csv

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lab05/svals/stats12_130.csv

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lab05/svals/stats12_131.csv

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lab05/svals/stats12_132.csv

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lab05/svals/stats12_133.csv

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lab05/svals/stats12_134.csv

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lab05/svals/stats12_135.csv

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lab05/svals/stats12_136.csv

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lab05/svals/stats12_137.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_138.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_139.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_14.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_140.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_141.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_142.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_143.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_144.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_145.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_146.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_147.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_148.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_149.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_15.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_150.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_151.csv

@@ -0,0 +1 @@
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lab05/svals/stats12_152.csv

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lab05/svals/stats12_153.csv

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lab05/svals/stats12_154.csv

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lab05/svals/stats12_155.csv

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lab05/svals/stats12_156.csv

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lab05/svals/stats12_157.csv

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lab05/svals/stats12_158.csv

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lab05/svals/stats12_159.csv

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lab05/svals/stats12_16.csv

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lab05/svals/stats12_160.csv

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lab05/svals/stats12_161.csv

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lab05/svals/stats12_162.csv

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lab05/svals/stats12_163.csv

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lab05/svals/stats12_164.csv

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lab05/svals/stats12_165.csv

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lab05/svals/stats12_166.csv

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lab05/svals/stats12_167.csv

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lab05/svals/stats12_168.csv

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lab05/svals/stats12_169.csv

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lab05/svals/stats12_17.csv

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lab05/svals/stats12_170.csv

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lab05/svals/stats12_171.csv

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lab05/svals/stats12_172.csv

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lab05/svals/stats12_173.csv

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lab05/svals/stats12_174.csv

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lab05/svals/stats12_175.csv

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lab05/svals/stats12_176.csv

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lab05/svals/stats12_177.csv

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lab05/svals/stats12_178.csv

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lab05/svals/stats12_179.csv

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lab05/svals/stats12_18.csv

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lab05/svals/stats12_180.csv

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lab05/svals/stats12_181.csv

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lab05/svals/stats12_182.csv

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