v1 version and measurements

homework_1/Makefile

@@ -27,8 +27,8 @@ SRC_DIR_LIST    := src gtest
 # Include directories list(space seperated). Makefile-relative path.
 INC_DIR_LIST    := inc \
                    src \
+                  /usr/include/hdf5/serial/ \
                    gtest \
-                   /usr/include/hdf5/serial/
 #                   Libs/MATLAB/R2019b/include/ \
 
 # Exclude files list(space seperated). Filenames only.
@@ -179,7 +179,7 @@ hpc-clean:
 	rm hpc-results/post
 
 #
-# ================ Local via docker build rules =================
+# ================ Local (and/or) via docker build rules =================
 #
 # examples:
 # make IMAGE=hpcimage v0
@@ -190,12 +190,6 @@ local_v0: CXXFLAGS := $(DEB_CXXFLAGS) -DCODE_VERSION=0
 local_v0: TARGET := local_v0
 local_v0: $(BUILD_DIR)/$(TARGET)
 	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
-
-local_v0_opt: CFLAGS := $(REL_CFLAGS) -DCODE_VERSION=0
-local_v0_opt: CXXFLAGS := $(REL_CXXFLAGS) -DCODE_VERSION=0
-local_v0_opt: TARGET := local_v0_opt
-local_v0_opt: $(BUILD_DIR)/$(TARGET)
-	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
 	
 local_v1: CFLAGS := $(DEB_CFLAGS) -DCODE_VERSION=1
 local_v1: CXXFLAGS := $(DEB_CXXFLAGS) -DCODE_VERSION=1
@@ -203,6 +197,21 @@ local_v1: TARGET := local_v1
 local_v1: $(BUILD_DIR)/$(TARGET)
 	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
 
+local_v1_omp: CFLAGS := $(DEB_CFLAGS) -fopenmp -DCODE_VERSION=1 -DOMP
+local_v1_omp: CXXFLAGS := $(DEB_CXXFLAGS) -fopenmp -DCODE_VERSION=1 -DOMP
+local_v1_omp: LDFLAGS += -fopenmp
+local_v1_omp: TARGET := local_v1_omp
+local_v1_omp: $(BUILD_DIR)/$(TARGET)
+	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
+
+
+local_v1_pth: CFLAGS := $(DEB_CFLAGS) -DCODE_VERSION=1 -DPTHREADS
+local_v1_pth: CXXFLAGS := $(DEB_CXXFLAGS) -DCODE_VERSION=1 -DPTHREADS
+local_v1_pth: TARGET := local_v1_pth
+local_v1_pth: $(BUILD_DIR)/$(TARGET)
+	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
+	
+
 v0: DOCKER := $(DOCKER_CMD)
 v0: CFLAGS := $(REL_CFLAGS) -DCODE_VERSION=0
 v0: CXXFLAGS := $(REL_CXXFLAGS) -DCODE_VERSION=0
@@ -215,7 +224,7 @@ v1_cilk: CXX    := /usr/local/OpenCilk-9.0.1-Linux/bin/clang++
 v1_cilk: CFLAGS := $(REL_CFLAGS) -fcilkplus -DCODE_VERSION=1 -DCILK
 v1_cilk: CXXFLAGS := $(REL_CXXFLAGS) -fcilkplus -DCODE_VERSION=1 -DCILK
 v1_cilk: LDFLAGS += -fcilkplus
-v1_cilk: TARGET := knnsearch_cilkv1
+v1_cilk: TARGET := knnsearch_v1_cilk
 v1_cilk: $(BUILD_DIR)/$(TARGET)
 	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
 
@@ -223,10 +232,17 @@ v1_omp: DOCKER := $(DOCKER_CMD)
 v1_omp: CFLAGS := $(REL_CFLAGS) -fopenmp -DCODE_VERSION=1 -DOMP
 v1_omp: CXXFLAGS := $(REL_CXXFLAGS) -fopenmp -DCODE_VERSION=1 -DOMP
 v1_omp: LDFLAGS += -fopenmp
-v1_omp: TARGET := knnsearch_ompv1
+v1_omp: TARGET := knnsearch_v1_omp
 v1_omp: $(BUILD_DIR)/$(TARGET)
 	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
 
+v1_pth: DOCKER := $(DOCKER_CMD)
+v1_pth: CFLAGS := $(REL_CFLAGS) -DCODE_VERSION=1 -DPTHREADS
+v1_pth: CXXFLAGS := $(REL_CXXFLAGS) -DCODE_VERSION=1 -DPTHREADS
+v1_pth: TARGET := knnsearch_v1_pth
+v1_pth: $(BUILD_DIR)/$(TARGET)
+	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
+	
 v1: DOCKER := $(DOCKER_CMD)
 v1: CFLAGS := $(REL_CFLAGS) -DCODE_VERSION=1
 v1: CXXFLAGS := $(REL_CXXFLAGS) -DCODE_VERSION=1
@@ -240,7 +256,12 @@ tests: TARGET := tests
 tests: $(BUILD_DIR)/$(TARGET)
 	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
 	
-	
+tests_rel: CFLAGS := $(REL_CFLAGS) -DCODE_VERSION=0 -DTESTING
+tests_rel: CXXFLAGS := $(REL_CXXFLAGS) -DCODE_VERSION=0 -DTESTING
+tests_rel: TARGET := tests
+tests_rel: $(BUILD_DIR)/$(TARGET)
+	cp $(BUILD_DIR)/$(TARGET) out/$(TARGET)
+
 #
 # ========= Inside CSAL Image build rules ===========
 #

homework_1/src/tests.cpp → homework_1/gtest/tests.cpp

@@ -19,6 +19,8 @@
 
 using matrix_t = mtx::Matrix<int>;
 
+extern void loadMtx(MatrixDst& Corpus, MatrixDst& Query);
+extern void storeMtx(MatrixIdx& Idx, MatrixDst& Dst);
 
 // =====================================
 // C1, Q1
@@ -140,11 +142,44 @@ TEST(Tv0_UT, pdist2_test2) {
 }
 
 
+TEST(Tv0_UT, pdist2_test3) {
+
+   mtx::Matrix<double> D2_exp(16, 16, {
+            0,  0.7433,  0.6868,  0.8846,  0.6342,  0.4561,  0.5118,  0.6341,  0.5461,  0.7322,  0.6974,  0.4330,  0.7028,  0.6303,  0.6826,  0.4179,
+       0.7433,       0,  0.3400,  0.4555,  0.4207,  0.9736,  0.9690,  0.7386,  1.1055,  0.5462,  0.5345,  0.6576,  0.8677,  1.0291,  0.5393,  0.8106,
+       0.6868,  0.3400,       0,  0.5380,  0.6268,  0.9512,  1.0234,  0.8403,  0.9843,  0.8187,  0.3091,  0.7829,  0.5759,  0.9411,  0.7239,  0.9186,
+       0.8846,  0.4555,  0.5380,       0,  0.6796,  1.1672,  1.0460,  1.1016,  1.1139,  0.7542,  0.6480,  0.9304,  1.0568,  1.3482,  0.8316,  0.9750,
+       0.6342,  0.4207,  0.6268,  0.6796,       0,  0.9267,  0.8772,  0.4847,  0.9317,  0.4093,  0.8351,  0.4215,  0.9736,  0.9007,  0.5999,  0.5291,
+       0.4561,  0.9736,  0.9512,  1.1672,  0.9267,       0,  0.3903,  0.7795,  0.9308,  0.8429,  0.8436,  0.5672,  0.9284,  0.7064,  0.6435,  0.5975,
+       0.5118,  0.9690,  1.0234,  1.0460,  0.8772,  0.3903,       0,  0.8920,  0.9253,  0.7060,  0.9427,  0.5728,  1.1515,  0.9907,  0.6471,  0.4811,
+       0.6341,  0.7386,  0.8403,  1.1016,  0.4847,  0.7795,  0.8920,       0,  0.9824,  0.6416,  0.9844,  0.3398,  0.9355,  0.5428,  0.6536,  0.5309,
+       0.5461,  1.1055,  0.9843,  1.1139,  0.9317,  0.9308,  0.9253,  0.9824,       0,  1.1517,  1.0541,  0.8746,  0.8506,  0.8777,  1.2036,  0.7607,
+       0.7322,  0.5462,  0.8187,  0.7542,  0.4093,  0.8429,  0.7060,  0.6416,  1.1517,       0,  0.9106,  0.4245,  1.2071,  1.0738,  0.3745,  0.5170,
+       0.6974,  0.5345,  0.3091,  0.6480,  0.8351,  0.8436,  0.9427,  0.9844,  1.0541,  0.9106,       0,  0.8647,  0.5941,  0.9954,  0.7148,  0.9876,
+       0.4330,  0.6576,  0.7829,  0.9304,  0.4215,  0.5672,  0.5728,  0.3398,  0.8746,  0.4245,  0.8647,       0,  0.9590,  0.6782,  0.4586,  0.2525,
+       0.7028,  0.8677,  0.5759,  1.0568,  0.9736,  0.9284,  1.1515,  0.9355,  0.8506,  1.2071,  0.5941,  0.9590,       0,  0.6838,  1.0517,  1.0675,
+       0.6303,  1.0291,  0.9411,  1.3482,  0.9007,  0.7064,  0.9907,  0.5428,  0.8777,  1.0738,  0.9954,  0.6782,  0.6838,       0,  0.9482,  0.7937,
+       0.6826,  0.5393,  0.7239,  0.8316,  0.5999,  0.6435,  0.6471,  0.6536,  1.2036,  0.3745,  0.7148,  0.4586,  1.0517,  0.9482,       0,  0.6345,
+       0.4179,  0.8106,  0.9186,  0.9750,  0.5291,  0.5975,  0.4811,  0.5309,  0.7607,  0.5170,  0.9876,  0.2525,  1.0675,  0.7937,  0.6345,       0
+   });
+
+   mtx::Matrix<double> D (16,16);
+
+   v0::pdist2(C2, C2, D);
+
+   for (size_t i = 0 ; i< D.rows() ; ++i)
+      for (size_t j = 0 ; j<D.columns() ; ++j) {
+         EXPECT_EQ (D2_exp.get(i ,j) + 0.01 > D(i, j), true);
+         EXPECT_EQ (D2_exp.get(i ,j) - 0.01 < D(i, j), true);
+      }
+}
+
+
 /*
  * ==========================================
  * v0::knn
  */
-TEST(Tv0_UT, knn_test1) {
+TEST(Tv0_UT, knn_v0_test1) {
    size_t k = 3;
    mtx::Matrix<uint32_t> Idx_exp(5, k, {
       5,     8,     9,
@@ -177,7 +212,7 @@ TEST(Tv0_UT, knn_test1) {
 
 }
 
-TEST(Tv0_UT, knn_test2) {
+TEST(Tv0_UT, knn_v0_test2) {
    size_t k = 3;
    mtx::Matrix<uint32_t> Idx_exp(8, k, {
       14,    13,     1,
@@ -220,7 +255,46 @@ TEST(Tv0_UT, knn_test2) {
  * ==========================================
  * v1::knn
  */
-TEST(Tv1_UT, knn_test1) {
+TEST(Tv1_UT, knn_v1_1slice) {
+   size_t k = 3;
+   mtx::Matrix<uint32_t> Idx_exp(8, k, {
+      14,    13,     1,
+      15,    10,    12,
+      14,     8,    12,
+       4,     1,     3,
+       8,    12,     5,
+       4,     3,     2,
+      10,     2,     4,
+       1,    11,     9
+   });
+
+   mtx::Matrix<double> Dst_exp(8, k, {
+      0.1939,    0.5768,    0.6020,
+      0.2708,    0.3686,    0.3740,
+      0.2684,    0.3103,    0.5277,
+      0.4709,    0.6050,    0.6492,
+      0.4397,    0.6842,    0.7074,
+      0.3402,    0.4360,    0.4475,
+      0.3708,    0.4037,    0.4417,
+      0.6352,    0.6653,    0.6758
+   });
+
+   mtx::Matrix<uint32_t> Idx(8, k);
+   mtx::Matrix<double>   Dst(8, k);
+
+   v1::knnsearch(C2, Q2, 1, k, k, Idx, Dst);
+
+
+   for (size_t i = 0 ; i< Idx.rows() ; ++i)
+      for (size_t j = 0 ; j<Idx.columns() ; ++j) {
+         EXPECT_EQ (Idx_exp(i ,j) == Idx(i, j) + 1, true);  // matlab starts from 1
+         EXPECT_EQ (Dst_exp.get(i ,j) + 0.01 > Dst(i, j), true);
+         EXPECT_EQ (Dst_exp.get(i ,j) - 0.01 < Dst(i, j), true);
+      }
+
+}
+
+TEST(Tv1_UT, knn_v1_2slice) {
    size_t k = 3;
    mtx::Matrix<uint32_t> Idx_exp(8, k, {
       14,    13,     1,
@@ -247,7 +321,7 @@ TEST(Tv1_UT, knn_test1) {
    mtx::Matrix<uint32_t> Idx(8, k);
    mtx::Matrix<double>   Dst(8, k);
 
-   v1::knnsearch(C2, Q2, 0, k, k, Idx, Dst);
+   v1::knnsearch(C2, Q2, 2, k, k, Idx, Dst);
 
 
    for (size_t i = 0 ; i< Idx.rows() ; ++i)
@@ -260,7 +334,7 @@ TEST(Tv1_UT, knn_test1) {
 }
 
 // all-to-all
-TEST(Tv1_UT, knn_test2) {
+TEST(Tv1_UT, knn_v1_4slice) {
    size_t k = 3;
    mtx::Matrix<uint32_t> Idx_exp(16, k, {
       1,    16,    12,
@@ -303,7 +377,7 @@ TEST(Tv1_UT, knn_test2) {
    mtx::Matrix<uint32_t> Idx(16, k);
    mtx::Matrix<double>   Dst(16, k);
 
-   v1::knnsearch(C2, C2, 0, k, k, Idx, Dst);
+   v1::knnsearch(C2, C2, 4, k, k, Idx, Dst);
 
 
    for (size_t i = 0 ; i< Idx.rows() ; ++i)
@@ -315,3 +389,130 @@ TEST(Tv1_UT, knn_test2) {
 
 }
 
+
+
+/*
+ * ============== Live hdf5 tests ===============
+ *
+ * In order to run these test we need the followin hdf5 files in ./mtx directory:
+ *
+ * - fasion-mnist-784-euclidean.hdf5
+ * - mnist-784-euclidean.hdf5
+ * - sift-128-euclidean.hdf5
+ * - gist-960-euclidean.hdf5
+ *
+ */
+
+TEST(Tlive_UT, knn_v0_sift_test) {
+   // Instantiate matrixes
+   MatrixDst Corpus;
+   MatrixDst Query;
+   MatrixIdx Idx;
+   MatrixDst Dst;
+
+   // setup environment
+   session.corpusMtxFile = "mtx/sift-128-euclidean.hdf5";
+   session.corpusDataSet = "/test";
+   session.queryMtx = false;
+   session.k = 100;
+   size_t m = session.k;
+   session.timing = true;
+   session.outMtxFile = "test/knn_v0.hdf5";
+
+
+   loadMtx(Corpus, Query);
+
+   // Prepare output memory (There is no Query, so from Corpus
+   Idx.resize(Corpus.rows(), session.k);
+   Dst.resize(Corpus.rows(), session.k);
+
+   v0::knnsearch(Corpus, Corpus, 0, session.k, m, Idx, Dst);
+   storeMtx(Idx, Dst);
+   EXPECT_EQ(true, true);
+}
+
+
+TEST(Tlive_UT, knn_v1_sift_test_1slice) {
+   // Instantiate matrixes
+   MatrixDst Corpus;
+   MatrixDst Query;
+   MatrixIdx Idx;
+   MatrixDst Dst;
+
+   // setup environment
+   session.corpusMtxFile = "mtx/sift-128-euclidean.hdf5";
+   session.corpusDataSet = "/test";
+   session.queryMtx = false;
+   session.k = 100;
+   size_t m = session.k;
+   session.timing = true;
+   session.outMtxFile = "test/knn_v1ser.hdf5";
+
+
+   loadMtx(Corpus, Query);
+
+   // Prepare output memory (There is no Query, so from Corpus
+   Idx.resize(Corpus.rows(), session.k);
+   Dst.resize(Corpus.rows(), session.k);
+
+   v1::knnsearch(Corpus, Corpus, 0, session.k, m, Idx, Dst);
+   storeMtx(Idx, Dst);
+   EXPECT_EQ(true, true);
+}
+
+TEST(Tlive_UT, knn_v1_sift_test_2slice) {
+   // Instantiate matrixes
+   MatrixDst Corpus;
+   MatrixDst Query;
+   MatrixIdx Idx;
+   MatrixDst Dst;
+
+   // setup environment
+   session.corpusMtxFile = "mtx/sift-128-euclidean.hdf5";
+   session.corpusDataSet = "/test";
+   session.queryMtx = false;
+   session.k = 100;
+   size_t m = session.k;
+   session.timing = true;
+   session.outMtxFile = "test/knn_v1ser.hdf5";
+
+
+   loadMtx(Corpus, Query);
+
+   // Prepare output memory (There is no Query, so from Corpus
+   Idx.resize(Corpus.rows(), session.k);
+   Dst.resize(Corpus.rows(), session.k);
+
+   v1::knnsearch(Corpus, Corpus, 2, session.k, m, Idx, Dst);
+   storeMtx(Idx, Dst);
+   EXPECT_EQ(true, true);
+}
+
+TEST(Tlive_UT, knn_v1_sift_test_4slice) {
+   // Instantiate matrixes
+   MatrixDst Corpus;
+   MatrixDst Query;
+   MatrixIdx Idx;
+   MatrixDst Dst;
+
+   // setup environment
+   session.corpusMtxFile = "mtx/sift-128-euclidean.hdf5";
+   session.corpusDataSet = "/test";
+   session.queryMtx = false;
+   session.k = 100;
+   size_t m = session.k;
+   session.timing = true;
+   session.outMtxFile = "test/knn_v1ser.hdf5";
+
+
+   loadMtx(Corpus, Query);
+
+   // Prepare output memory (There is no Query, so from Corpus
+   Idx.resize(Corpus.rows(), session.k);
+   Dst.resize(Corpus.rows(), session.k);
+
+   v1::knnsearch(Corpus, Corpus, 4, session.k, m, Idx, Dst);
+   storeMtx(Idx, Dst);
+   EXPECT_EQ(true, true);
+}
+

homework_1/inc/config.h

@@ -55,7 +55,9 @@ struct session_t {
    std::string   outMtxFile     {"out.hdf5"};              //!< output matrix file name in HDF5 format
    std::string   outMtxIdxDataSet {"/Idx"};                //!< Index output dataset name in HDF5 matrix file
    std::string   outMtxDstDataSet {"/Dst"};                //!< Distance output dataset name in HDF5 matrix file
-   std::size_t   max_threads    {};                        //!< Maximum threads to use
+   std::size_t   max_threads    {0};                       //!< Maximum threads to use
+   std::size_t   slices         {0};                       //!< Slices/threads to use
+   std::size_t   accuracy       {100};                     //!< The neighbor finding accuracy
    bool          timing         {false};                   //!< Enable timing prints of the program
    bool          verbose        {false};                   //!< Flag to enable verbose output to stdout
 };

homework_1/inc/matrix.hpp

@@ -134,6 +134,9 @@ struct Matrix {
    Matrix& operator=(Matrix&& m) noexcept { moves(std::move(m)); return *this; }
    Matrix(const Matrix& m)             = delete;  //!< No copy ctor
    Matrix& operator=(const Matrix& m)  = delete;  //!< No copy
+   //Matrix(const Matrix& m);
+   //Matrix& operator=(const Matrix& m) { copy(m); }
+
    //! @}
 
    //! \name Data exposure
@@ -233,6 +236,11 @@ struct Matrix {
 
    // a basic serial iterator support
    DataType* data() noexcept { return data_; }
+   DataType* begin() noexcept { return data_; }
+   const DataType* begin() const noexcept { return data_; }
+   DataType* end() noexcept { return data_ + capacity(rows_, cols_); }
+   const DataType* end() const noexcept { return data_ + capacity(rows_, cols_); }
+
 //   IndexType begin_idx() noexcept { return 0; }
 //   IndexType end_idx()   noexcept { return capacity(rows_, cols_); }
 
@@ -265,17 +273,19 @@ struct Matrix {
       std::swap(rows_, src.rows_);
       std::swap(cols_, src.cols_);
    }
+
 private:
    //! move helper
    void moves(Matrix&& src) noexcept {
-      data_ = std::move(src.vector_storage_);
-      data_ = std::move(src.raw_storage_);
-      data_ = std::move(src.data_);
-      data_ = std::move(src.use_vector_);
-      rows_ = std::move(src.rows_);
-      cols_ = std::move(src.cols_);
+      vector_storage_ = std::move(src.vector_storage_);
+      raw_storage_    = std::move(src.raw_storage_);
+      data_           = std::move(src.data_);
+      use_vector_     = std::move(src.use_vector_);
+      rows_           = std::move(src.rows_);
+      cols_           = std::move(src.cols_);
    }
 
+   // Storage
    std::vector<DataType>
                vector_storage_;  //!< Internal storage (if used).
    DataType*   raw_storage_;     //!< External storage (if used).
@@ -528,125 +538,6 @@ private:
 };
 
 
-template<typename ...> struct Matrix_view { };
-
-/*!
- * @struct Matrix_view
- * @tparam MatrixType
- */
-template<template <typename, typename, MatrixType, MatrixOrder, bool> class Matrix,
-         typename DataType,
-         typename IndexType,
-         MatrixType Type,
-         MatrixOrder Order>
-struct Matrix_view<Matrix<DataType, IndexType, Type, Order, false>> {
-   using owner_t = Matrix<DataType, IndexType, Type, Order, false>;
-
-   using dataType    = DataType;                      //!< meta:export of underling data type
-   using indexType   = IndexType;                     //!< meta:export of underling index type
-   static constexpr MatrixOrder matrixOrder = Order;  //!< meta:export of array order
-   static constexpr MatrixType matrixType = Type;     //!< meta:export of array type
-
-   /*!
-    * \name Obj lifetime
-    */
-   //! @{
-
-   //! Construct a matrix view to entire matrix
-   Matrix_view(const owner_t* owner) noexcept :
-      owner_(owner), m_(owner->data()), rows_(owner->rows()), cols_(owner->columns()) { }
-
-   Matrix_view(const owner_t* owner, IndexType begin, IndexType end) noexcept :
-      owner_(owner) {
-         if constexpr (Order == MatrixOrder::ROWMAJOR) {
-            m_ = owner->data() + begin * owner->columns();
-            rows_ = end - begin;
-            cols_ = owner->columns();
-         } else if (Order == MatrixOrder::COLMAJOR) {
-            m_ = owner->data() + begin * owner->rows();
-            rows_ = owner->rows();
-            cols_ = end - begin;
-         }
-   }
-
-   Matrix_view(Matrix_view&& m)                 = delete; //! No move
-   Matrix_view& operator=(Matrix_view&& m)      = delete;
-   Matrix_view(const Matrix_view& m)            = delete; //!< No copy
-   Matrix_view& operator=(const Matrix_view& m) = delete;
-   //! @}
-
-   //! Get/Set the size of each dimension
-   const IndexType rows() const noexcept { return rows_; }
-   const IndexType columns() const noexcept { return cols_; }
-
-   //! Get the interface size of the Matrix (what appears to be the size)
-   IndexType size() const {
-      return rows_ * cols_;
-   }
-
-   //! Actual memory capacity of the symmetric matrix
-   static constexpr IndexType capacity(IndexType M, IndexType N) {
-      return M*N;
-   }
-   /*
-    * virtual 2D accessors
-    */
-   const DataType get (IndexType i, IndexType j) const {
-      if constexpr (Order == MatrixOrder::COLMAJOR)
-         return m_[i + j*rows_];
-      else
-         return m_[i*cols_ + j];
-   }
-
-   DataType set (DataType v, IndexType i, IndexType j) {
-      if constexpr (Order == MatrixOrder::COLMAJOR)
-         return m_[i + j*rows_] = v;
-      else
-         return m_[i*cols_ + j] = v;
-   }
-//   DataType operator()(IndexType i, IndexType j) { return get(i, j); }
-   /*!
-    * Return a proxy MatVal object with read and write capabilities.
-    * @param i    The row number
-    * @param j    The column number
-    * @return     tHE MatVal object
-    */
-   MatVal<Matrix_view> operator()(IndexType i, IndexType j) noexcept {
-      return MatVal<Matrix_view>(this, get(i, j), i, j);
-   }
-
-   // a basic serial iterator support
-   DataType* data() noexcept { return m_.data(); }
-//   IndexType begin_idx() noexcept { return 0; }
-//   IndexType end_idx()   noexcept { return capacity(rows_, cols_); }
-
-   const DataType* data() const noexcept { return m_; }
-   const IndexType begin_idx() const noexcept { return 0; }
-   const IndexType end_idx()   const noexcept { return capacity(rows_, cols_); }
-   //! @}
-
-   /*!
-    * \name Safe iteration API
-    *
-    * This api automates the iteration over the array based on
-    * MatrixType
-    */
-   //! @{
-   template<typename F, typename... Args>
-   void for_each_in (IndexType begin, IndexType end, F&& lambda, Args&&... args) {
-      for (IndexType it=begin ; it<end ; ++it) {
-         std::forward<F>(lambda)(std::forward<Args>(args)..., it);
-      }
-   }
-   //! @}
-   //!
-private:
-   const owner_t* owner_ {nullptr};     //!< Pointer to Matrix
-   DataType*   m_       {nullptr};     //!< Starting address of the slice/view
-   IndexType   rows_{};                //!< the virtual size of rows.
-   IndexType   cols_{};                //!< the virtual size of columns.
-};
-
 /*!
  * A view/iterator hybrid object for Matrix columns.
  *

homework_1/inc/v0.hpp

@@ -54,10 +54,11 @@ void pdist2(const Matrix& X, const Matrix& Y, Matrix& D2) {
    for (int i = 0; i < M ; ++i) {
       for (int j = 0; j < N; ++j) {
          D2.set(D2.get(i, j) + X_norms[i] + Y_norms[j], i, j);
-         //D2.set(std::max(D2.get(i, j), 0.0),            i, j); // Ensure non-negative
+         D2.set(std::max(D2.get(i, j), 0.0),            i, j); // Ensure non-negative
          D2.set(std::sqrt(D2.get(i, j)),                i, j); // Take the square root of each
       }
    }
+   M++;
 }
 
 template<typename DataType, typename IndexType>
@@ -82,7 +83,7 @@ void quickselect(std::vector<std::pair<DataType, IndexType>>& vec, int k) {
  *             point of Q
  */
 template<typename MatrixD, typename MatrixI>
-void knnsearch(const MatrixD& C, const MatrixD& Q, size_t idx_offset, size_t k, size_t m, MatrixI& idx, MatrixD& dst) {
+void knnsearch(MatrixD& C, MatrixD& Q, size_t idx_offset, size_t k, size_t m, MatrixI& idx, MatrixD& dst) {
 
    using DstType = typename MatrixD::dataType;
    using IdxType = typename MatrixI::dataType;

homework_1/inc/v1.hpp

@@ -1,5 +1,5 @@
 /**
- * \file    v0.hpp
+ * \file    v1.hpp
  * \brief
  *
  * \author
@@ -16,6 +16,26 @@
 #include "v0.hpp"
 #include "config.h"
 
+#if defined CILK
+#include <cilk/cilk.h>
+#include <cilk/cilk_api.h>
+//#include <cilk/reducer_opadd.h>
+
+#elif defined OMP
+#include <omp.h>
+
+#elif defined PTHREADS
+#include <thread>
+#include <numeric>
+#include <functional>
+//#include <random>
+
+#else
+#endif
+
+
+void init_workers();
+
 namespace v1 {
 
 template <typename DataType, typename IndexType>
@@ -57,49 +77,110 @@ void mergeResultsWithM(mtx::Matrix<IndexType>& N1, mtx::Matrix<DataType>& D1,
     }
 }
 
-
-
 template<typename MatrixD, typename MatrixI>
-void knnsearch(const MatrixD& C, const MatrixD& Q, size_t idx_offset, size_t k, size_t m, MatrixI& idx, MatrixD& dst) {
-
+void worker_body (std::vector<MatrixD>& corpus_slices,
+                  std::vector<MatrixD>& query_slices,
+                  MatrixI& idx,
+                  MatrixD& dst,
+                  size_t slice,
+                  size_t num_slices, size_t corpus_slice_size, size_t query_slice_size,
+                  size_t k,
+                  size_t m) {
+   // "load" types
    using DstType = typename MatrixD::dataType;
    using IdxType = typename MatrixI::dataType;
 
-    if (C.rows() <= 8 || Q.rows() <= 4) {
-        // Base case: Call knnsearch directly
-        v0::knnsearch(C, Q, idx_offset, k, m, idx, dst);
-        return;
-    }
 
-    // Divide Corpus and Query into subsets
-    IdxType midC = C.rows() / 2;
-    IdxType midQ = Q.rows() / 2;
-
-    // Slice corpus and query matrixes
-    MatrixD C1((DstType*)C.data(), 0,    midC, C.columns());
-    MatrixD C2((DstType*)C.data(), midC, midC, C.columns());
-    MatrixD Q1((DstType*)Q.data(), 0,    midQ, Q.columns());
-    MatrixD Q2((DstType*)Q.data(), midQ, midQ, Q.columns());
-
-    // Allocate temporary matrixes for all permutations
-    MatrixI N1_1(midQ, k), N1_2(midQ, k), N2_1(midQ, k), N2_2(midQ, k);
-    MatrixD D1_1(midQ, k), D1_2(midQ, k), D2_1(midQ, k), D2_2(midQ, k);
-
-    // Recursive calls
-    knnsearch(C1, Q1, idx_offset,        k, m, N1_1, D1_1);
-    knnsearch(C2, Q1, idx_offset + midC, k, m, N1_2, D1_2);
-    knnsearch(C1, Q2, idx_offset,        k, m, N2_1, D2_1);
-    knnsearch(C2, Q2, idx_offset + midC, k, m, N2_2, D2_2);
-
-    // slice output matrixes
-    MatrixI N1((IdxType*)idx.data(), 0,    midQ, k);
-    MatrixI N2((IdxType*)idx.data(), midQ, midQ, k);
-    MatrixD D1((DstType*)dst.data(), 0,    midQ, k);
-    MatrixD D2((DstType*)dst.data(), midQ, midQ, k);
-
-    // Merge results in place
-    mergeResultsWithM(N1_1, D1_1, N1_2, D1_2, k, m, N1, D1);
-    mergeResultsWithM(N2_1, D2_1, N2_2, D2_2, k, m, N2, D2);
+   for (size_t ci = 0; ci < num_slices; ++ci) {
+      size_t idx_offset = ci * corpus_slice_size;
+
+      // Intermediate matrixes for intermediate results
+      MatrixI temp_idx(query_slices[slice].rows(), k);
+      MatrixD temp_dst(query_slices[slice].rows(), k);
+
+      // kNN for each combination
+      v0::knnsearch(corpus_slices[ci], query_slices[slice], idx_offset, k, m, temp_idx, temp_dst);
+
+      // Merge temporary results to final results
+      MatrixI idx_slice((IdxType*)idx.data(), slice * query_slice_size, query_slices[slice].rows(), k);
+      MatrixD dst_slice((DstType*)dst.data(), slice * query_slice_size, query_slices[slice].rows(), k);
+
+      mergeResultsWithM(idx_slice, dst_slice, temp_idx, temp_dst, k, m, idx_slice, dst_slice);
+   }
+}
+
+template<typename MatrixD, typename MatrixI>
+void knnsearch(MatrixD& C, MatrixD& Q, size_t num_slices, size_t k, size_t m, MatrixI& idx, MatrixD& dst) {
+    using DstType = typename MatrixD::dataType;
+    using IdxType = typename MatrixI::dataType;
+
+    //Slice calculations
+    size_t corpus_slice_size = C.rows() / ((num_slices == 0)? 1:num_slices);
+    size_t query_slice_size = Q.rows() / ((num_slices == 0)? 1:num_slices);
+
+    // Make slices
+    std::vector<MatrixD> corpus_slices;
+    std::vector<MatrixD> query_slices;
+
+   for (size_t i = 0; i < num_slices; ++i) {
+      corpus_slices.emplace_back(
+         (DstType*)C.data(),
+         i * corpus_slice_size,
+         (i == num_slices - 1 ? C.rows() - i * corpus_slice_size : corpus_slice_size),
+         C.columns());
+      query_slices.emplace_back(
+         (DstType*)Q.data(),
+         i * query_slice_size,
+         (i == num_slices - 1 ? Q.rows() - i * query_slice_size : query_slice_size),
+         Q.columns());
+   }
+
+   // Intermediate results
+   for (size_t i = 0; i < dst.rows(); ++i) {
+      for (size_t j = 0; j < dst.columns(); ++j) {
+         dst.set(std::numeric_limits<DstType>::infinity(), i, j);
+         idx.set(static_cast<IdxType>(-1), i, j);
+      }
+   }
+
+    // Main loop
+#if defined OMP
+   #pragma omp parallel for
+   for (size_t qi = 0; qi < num_slices; ++qi) {
+      for (size_t qi = 0; qi < num_slices; ++qi) {
+         worker_body (corpus_slices, query_slices, idx, dst, qi, num_slices, corpus_slice_size, query_slice_size, k, m);
+      }
+   }
+#elif defined CILK
+   cilk_for (size_t qi = 0; qi < num_slices; ++qi) {
+      for (size_t qi = 0; qi < num_slices; ++qi) {
+         worker_body (corpus_slices, query_slices, idx, dst, qi, num_slices, corpus_slice_size, query_slice_size, k, m);
+      }
+   }
+#elif defined PTHREADS
+   std::vector<std::thread> workers;
+   for (size_t qi = 0; qi < num_slices; ++qi) {
+      workers.push_back(
+         std::thread (worker_body<MatrixD, MatrixI>,
+                      std::ref(corpus_slices), std::ref(query_slices),
+                      std::ref(idx), std::ref(dst),
+                      qi,
+                      num_slices, corpus_slice_size, query_slice_size,
+                      k, m)
+      );
+   }
+   // Join threads
+   std::for_each(workers.begin(), workers.end(), [](std::thread& t){
+      t.join();
+   });
+
+#else
+   for (size_t qi = 0; qi < num_slices; ++qi) {
+      for (size_t qi = 0; qi < num_slices; ++qi) {
+         worker_body (corpus_slices, query_slices, idx, dst, qi, num_slices, corpus_slice_size, query_slice_size, k, m);
+      }
+   }
+#endif
 
 }
 

homework_1/matlab/dist2.m

@@ -12,7 +12,10 @@ function [D2] = dist2(X, Y)
     if d1 ~= d2
         error('X,Y column dimensions must match');
     end
-    %D2 = sqrt((X.^2)*ones(d,m) -2*X*Y' + ones(n,d)*(Y.^2)');
+    % debug
+    %X_norm = sum(X.^2, 2);
+    %Y_norm = sum(Y.^2, 2)';
+    %XY = 2 * X*Y';
     D2 = max(sum(X.^2, 2) - 2 * X*Y' + sum(Y.^2, 2)', 0);
     D2 = sqrt(D2);
     

homework_1/matlab/measurements_over_acc.m

@@ -0,0 +1,78 @@
+% Plot measurements
+
+accuracy = [100 80 60 40 20 10];
+ser_sift_acc = [ 4395 4365 4384 4315 4295 4246 ];
+ser_mnist_ser_acc = [ 7936 7924 7886 7903 7844 7801 ];
+
+omp_sift_acc = [
+    4093 4098 4040 4001 3980 3937
+];
+omp_mnist_acc = [
+    7575 7463 7389 7416 7321 7303
+];
+    
+cilk_sift_acc = [
+    3718 3739 3673 3668 3608 3557
+];
+cilk_mnist_acc = [
+    7064 7071 7035 6948 6962 6913
+];
+
+pth_sift_acc = [
+    1157 1159 1121 1100 1084 1075
+];
+pth_mnist_acc = [
+    2050 2086 2040 2020 2004 1979
+];
+
+% 1ο Διάγραμμα: OMP
+figure;
+set(gcf, 'Position', [100, 100, 1280, 720]); % Set the figure size to HD
+plot(accuracy, ser_sift_acc, '-o', 'DisplayName', 'Serial SIFT');
+hold on;
+plot(accuracy, ser_mnist_ser_acc, '-s', 'DisplayName', 'Serial MNIST');
+plot(accuracy, omp_sift_acc, '-^', 'DisplayName', 'OMP SIFT');
+plot(accuracy, omp_mnist_acc, '-d', 'DisplayName', 'OMP MNIST');
+hold off;
+title('OMP');
+xlabel('Accuracy (%)');
+ylabel('Execution Time [msec]');
+set(gca, 'XDir', 'reverse'); % reverse x
+legend('Location', 'northwest');
+grid on;
+print(gcf, 'OMP_over_accuracy.png', '-dpng', '-r300');
+
+% 2ο Διάγραμμα: CILK
+figure;
+set(gcf, 'Position', [100, 100, 1280, 720]); % Set the figure size to HD
+plot(accuracy, ser_sift_acc, '-o', 'DisplayName', 'Serial SIFT');
+hold on;
+plot(accuracy, ser_mnist_ser_acc, '-s', 'DisplayName', 'Serial MNIST');
+plot(accuracy, cilk_sift_acc, '-^', 'DisplayName', 'CILK SIFT');
+plot(accuracy, cilk_mnist_acc, '-d', 'DisplayName', 'CILK MNIST');
+hold off;
+title('CILK');
+xlabel('Accuracy (%)');
+ylabel('Execution Time [msec]');
+set(gca, 'XDir', 'reverse'); % reverse x
+legend('Location', 'northwest');
+grid on;
+print(gcf, 'CILK_over_accuracy.png', '-dpng', '-r300');
+
+% 3ο Διάγραμμα: Pthreads
+figure;
+set(gcf, 'Position', [100, 100, 1280, 720]); % Set the figure size to HD
+plot(accuracy, ser_sift_acc, '-o', 'DisplayName', 'Serial SIFT');
+hold on;
+plot(accuracy, ser_mnist_ser_acc, '-s', 'DisplayName', 'Serial MNIST');
+plot(accuracy, pth_sift_acc, '-^', 'DisplayName', 'Pthreads SIFT');
+plot(accuracy, pth_mnist_acc, '-d', 'DisplayName', 'Pthreads MNIST');
+hold off;
+title('Pthreads');
+xlabel('Accuracy (%)');
+ylabel('Execution Time [msec]');
+set(gca, 'XDir', 'reverse'); % reverse x
+legend('Location', 'northwest');
+grid on;
+print(gcf, 'Pthreads_over_accuracy.png', '-dpng', '-r300');
+

homework_1/matlab/measurements_over_th.m

@@ -0,0 +1,75 @@
+% Plot measurements
+
+threads = [1 2 4 6 8 10 12];
+ser_sift_threads = [ 4418 4418 4418 4418 4418 4418 4418 ];
+ser_mnist_ser_threads = [ 7924 7924 7924 7924 7924 7924 7924 ];
+
+omp_sift_th = [
+    4469 4283 4096 3822 4060 4241 5193
+];
+omp_mnist_th = [
+    8053 7806 7465 6828 7662 8013 8123
+];
+    
+cilk_sift_th = [
+    4225 4090 3696 3122 3860 4141 5103
+];
+cilk_mnist_th = [
+    7744 7206 6965 6628 7362 7813 8123
+];
+
+pth_sift_th = [
+    4254 2155 1133 877 724 640 682
+];
+pth_mnist_th = [
+    7889 3963 2058 1445 1496 1379 1352
+];
+
+% 1ο Διάγραμμα: OMP
+figure;
+set(gcf, 'Position', [100, 100, 1280, 720]); % Set the figure size to HD
+plot(threads, ser_sift_threads, '-o', 'DisplayName', 'Serial SIFT');
+hold on;
+plot(threads, ser_mnist_ser_threads, '-s', 'DisplayName', 'Serial MNIST');
+plot(threads, omp_sift_th, '-^', 'DisplayName', 'OMP SIFT');
+plot(threads, omp_mnist_th, '-d', 'DisplayName', 'OMP MNIST');
+hold off;
+title('OMP');
+xlabel('Threads');
+ylabel('Execution Time [msec]');
+legend('Location', 'northeast');
+grid on;
+print(gcf, 'OMP_over_threads.png', '-dpng', '-r300');
+
+% 2ο Διάγραμμα: CILK
+figure;
+set(gcf, 'Position', [100, 100, 1280, 720]); % Set the figure size to HD
+plot(threads, ser_sift_threads, '-o', 'DisplayName', 'Serial SIFT');
+hold on;
+plot(threads, ser_mnist_ser_threads, '-s', 'DisplayName', 'Serial MNIST');
+plot(threads, cilk_sift_th, '-^', 'DisplayName', 'CILK SIFT');
+plot(threads, cilk_mnist_th, '-d', 'DisplayName', 'CILK MNIST');
+hold off;
+title('CILK');
+xlabel('Threads');
+ylabel('Execution Time [msec]');
+legend('Location', 'northeast');
+grid on;
+print(gcf, 'CILK_over_threads.png', '-dpng', '-r300');
+
+% 3ο Διάγραμμα: Pthreads
+figure;
+set(gcf, 'Position', [100, 100, 1280, 720]); % Set the figure size to HD
+plot(threads, ser_sift_threads, '-o', 'DisplayName', 'Serial SIFT');
+hold on;
+plot(threads, ser_mnist_ser_threads, '-s', 'DisplayName', 'Serial MNIST');
+plot(threads, pth_sift_th, '-^', 'DisplayName', 'Pthreads SIFT');
+plot(threads, pth_mnist_th, '-d', 'DisplayName', 'Pthreads MNIST');
+hold off;
+title('Pthreads');
+xlabel('Threads');
+ylabel('Execution Time [msec]');
+legend('Location', 'northeast');
+grid on;
+print(gcf, 'Pthreads_over_threads.png', '-dpng', '-r300');
+

homework_1/matlab/run_test.m

@@ -2,6 +2,106 @@
 %
 %
 
+
+%
+%
+%
+C1 = [ 
+    0.8147    0.1576;
+    0.9058    0.9706;
+    0.1270    0.9572;
+    0.9134    0.4854;
+    0.6324    0.8003;
+    0.0975    0.1419;
+    0.2785    0.4218;
+    0.5469    0.9157;
+    0.9575    0.7922;
+    0.9649    0.9595 ];
+
+Q1 = [
+    0.6557    0.7577;
+    0.0357    0.7431;
+    0.8491    0.3922;
+    0.9340    0.6555;
+    0.6787    0.1712 ];
+
+C2 = [
+    0.7060    0.4456    0.5060    0.6160;
+    0.0318    0.6463    0.6991    0.4733;
+    0.2769    0.7094    0.8909    0.3517;
+    0.0462    0.7547    0.9593    0.8308;
+    0.0971    0.2760    0.5472    0.5853;
+    0.8235    0.6797    0.1386    0.5497;
+    0.6948    0.6551    0.1493    0.9172;
+    0.3171    0.1626    0.2575    0.2858;
+    0.9502    0.1190    0.8407    0.7572;
+    0.0344    0.4984    0.2543    0.7537;
+    0.4387    0.9597    0.8143    0.3804;
+    0.3816    0.3404    0.2435    0.5678;
+    0.7655    0.5853    0.9293    0.0759;
+    0.7952    0.2238    0.3500    0.0540;
+    0.1869    0.7513    0.1966    0.5308;
+    0.4898    0.2551    0.2511    0.7792 ];
+
+Q2 = [
+    0.9340    0.3112    0.4505    0.0782;
+    0.1299    0.5285    0.0838    0.4427;
+    0.5688    0.1656    0.2290    0.1067;
+    0.4694    0.6020    0.9133    0.9619;
+    0.0119    0.2630    0.1524    0.0046;
+    0.3371    0.6541    0.8258    0.7749;
+    0.1622    0.6892    0.5383    0.8173;
+    0.7943    0.7482    0.9961    0.8687 ];
+
+D1_exp = [
+    0.6208    0.9745    0.2371    0.5120    0.1367;
+    0.3284    0.8993    0.5811    0.3164    0.8310;
+    0.5651    0.2327    0.9169    0.8616    0.9603;
+    0.3749    0.9147    0.1132    0.1713    0.3921;
+    0.0485    0.5994    0.4621    0.3346    0.6308;
+    0.8312    0.6044    0.7922    0.9815    0.5819;
+    0.5052    0.4028    0.5714    0.6959    0.4722;
+    0.1919    0.5395    0.6045    0.4665    0.7561;
+    0.3037    0.9231    0.4144    0.1387    0.6807;
+    0.3692    0.9540    0.5790    0.3056    0.8386 ];
+  
+  
+  D2_exp = [
+    0.6020    0.7396    0.6583    0.6050    1.0070    0.5542    0.6298    0.6352;
+    1.0696    0.6348    0.9353    0.6914    0.8160    0.4475    0.4037    0.9145;
+    0.9268    0.8450    0.9376    0.6492    0.9671    0.4360    0.5956    0.7400;
+    1.3455    0.9876    1.2953    0.4709    1.2557    0.3402    0.4417    0.7500;
+    0.9839    0.5476    0.7517    0.7216    0.7074    0.5605    0.4784    0.9954;
+    0.6839    0.7200    0.7305    0.9495    1.0628    0.8718    0.8178    0.9179;
+    0.9850    0.7514    0.9585    0.7996    1.2054    0.7784    0.6680    0.8591;
+    0.6950    0.4730    0.3103    1.0504    0.4397    0.8967    0.8140    1.2066;
+    0.8065    1.2298    0.9722    0.7153    1.3933    0.8141    1.0204    0.6758;
+    1.1572    0.3686    0.9031    0.8232    0.7921    0.6656    0.3708    1.0970;
+    0.9432    0.9049    1.0320    0.6905    1.1167    0.5094    0.6455    0.6653;
+    0.7672    0.3740    0.5277    0.8247    0.6842    0.6945    0.5648    0.9968;
+    0.5768    1.1210    0.8403    0.9345    1.1316    0.8292    1.0380    0.8127;
+    0.1939    0.8703    0.2684    1.1794    0.8103    1.0683    1.1115    1.1646;
+    1.0106    0.2708    0.8184    0.8954    0.7402    0.6982    0.4509    1.0594;
+    0.8554    0.5878    0.6834    0.7699    0.9155    0.7161    0.6162    0.9481 ];
+
+% tests
+D1 = dist2(C1, Q1);
+if norm (D1-pdist2(C1, Q1), 'fro') > 0.01
+    disp('Error in dist2(C1, Q1)');
+end
+
+D2 = dist2(C2, Q2);
+if norm (D2-pdist2(C2, Q2), 'fro') > 0.01
+    disp('Error in dist2(C2, Q2)');
+end
+
+D2 = dist2(C2, C2);
+if norm (D2-pdist2(C2, C2), 'fro') > 0.01
+    disp('Error in dist2(C2, C2)');
+end
+    
+
+
 %C = rand(10000, 2);  % Corpus
 %Q = rand(10000, 2);   % Queries
 C = rand(20000, 2);  % Δύο clusters

homework_1/measurements/over_acc.txt

@@ -0,0 +1,122 @@
+# Serial-sift over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -a 100 -k 100 -t
+[Timing]: knnsearch: 4395 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -a 80 -k 100 -t
+[Timing]: knnsearch: 4365 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -a 60 -k 100 -t
+[Timing]: knnsearch: 4384 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -a 40 -k 100 -t
+[Timing]: knnsearch: 4315 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -a 20 -k 100 -t
+[Timing]: knnsearch: 4295 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -a 10 -k 100 -t
+[Timing]: knnsearch: 4246 [msec]
+
+
+# Serial-mnist over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -a 100 -k 100 -t
+[Timing]: knnsearch: 7936 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -a 80 -k 100 -t
+[Timing]: knnsearch: 7924 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -a 60 -k 100 -t
+[Timing]: knnsearch: 7886 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -a 40 -k 100 -t
+[Timing]: knnsearch: 7903 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -a 20 -k 100 -t
+[Timing]: knnsearch: 7844 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -a 10 -k 100 -t
+[Timing]: knnsearch: 7801 [msec]
+
+
+# OMP-sift over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 100 -k 100 -t
+[Timing]: knnsearch: 4093 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 80 -k 100 -t
+[Timing]: knnsearch: 4098 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 60 -k 100 -t
+[Timing]: knnsearch: 4040 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 40 -k 100 -t
+[Timing]: knnsearch: 4001 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 20 -k 100 -t
+[Timing]: knnsearch: 3980 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 10 -k 100 -t
+[Timing]: knnsearch: 3937 [msec]
+
+# OMP-mnist over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 100 -k 100 -t
+[Timing]: knnsearch: 7575 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 80 -k 100 -t
+[Timing]: knnsearch: 7463 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 60 -k 100 -t
+[Timing]: knnsearch: 7389 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 40 -k 100 -t
+[Timing]: knnsearch: 7416 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 20 -k 100 -t
+[Timing]: knnsearch: 7321 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 10 -k 100 -t
+[Timing]: knnsearch: 7303 [msec]
+
+
+
+# CILK-sift over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 100 -k 100 -t
+[Timing]: knnsearch: 4218 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 80 -k 100 -t
+[Timing]: knnsearch: 4239 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 60 -k 100 -t
+[Timing]: knnsearch: 4173 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 40 -k 100 -t
+[Timing]: knnsearch: 4168 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 20 -k 100 -t
+[Timing]: knnsearch: 4108 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 10 -k 100 -t
+[Timing]: knnsearch: 4057 [msec]
+
+
+# CILK-mnist over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 100 -k 100 -t
+[Timing]: knnsearch: 7864 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 80 -k 100 -t
+[Timing]: knnsearch: 7871 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 60 -k 100 -t
+[Timing]: knnsearch: 7835 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 40 -k 100 -t
+[Timing]: knnsearch: 7748 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 20 -k 100 -t
+[Timing]: knnsearch: 7762 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 10 -k 100 -t
+[Timing]: knnsearch: 7713 [msec]
+
+
+# Pthreads-sift over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 100 -k 100 -t
+[Timing]: knnsearch: 1157 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 80 -k 100 -t
+[Timing]: knnsearch: 1159 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 60 -k 100 -t
+[Timing]: knnsearch: 1121 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 40 -k 100 -t
+[Timing]: knnsearch: 1100 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 20 -k 100 -t
+[Timing]: knnsearch: 1084 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 4 -a 10 -k 100 -t
+[Timing]: knnsearch: 1075 [msec]
+
+
+# Pthreads-mnist over acc
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 100 -k 100 -t
+[Timing]: knnsearch: 2050 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 80 -k 100 -t
+[Timing]: knnsearch: 2086 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 60 -k 100 -t
+[Timing]: knnsearch: 2040 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 40 -k 100 -t
+[Timing]: knnsearch: 2020 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 20 -k 100 -t
+[Timing]: knnsearch: 2004 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -a 10 -k 100 -t
+[Timing]: knnsearch: 1979 [msec]
+
+
+
+

homework_1/measurements/over_threads.txt

@@ -0,0 +1,101 @@
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 4418 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 7924 [msec]
+
+# OMP-sift over threads
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 4469 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 2 -k 100 -t
+[Timing]: knnsearch: 4283 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 4 -k 100 -t
+[Timing]: knnsearch: 4096 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 6 -k 100 -t
+[Timing]: knnsearch: 3822 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 8 -k 100 -t
+[Timing]: knnsearch: 4060 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 10 -k 100 -t
+[Timing]: knnsearch: 4241 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 12 -k 100 -t
+[Timing]: knnsearch: 5193 [msec]
+
+# OMP-mnist over threads
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 8053 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 2 -k 100 -t
+[Timing]: knnsearch: 7806 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -k 100 -t
+[Timing]: knnsearch: 7465 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 6 -k 100 -t
+[Timing]: knnsearch: 6828 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 8 -k 100 -t
+[Timing]: knnsearch: 7662[msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 10 -k 100 -t
+[Timing]: knnsearch: 8013 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 12 -k 100 -t
+[Timing]: knnsearch: 8123 [msec]
+
+# CILK-sift over threads
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 4225 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 2 -k 100 -t
+[Timing]: knnsearch: 4090 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 4 -k 100 -t
+[Timing]: knnsearch: 3696 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 6 -k 100 -t
+[Timing]: knnsearch: 3122 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 8 -k 100 -t
+[Timing]: knnsearch: 3860 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 10 -k 100 -t
+[Timing]: knnsearch: 4141 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/sift-128-euclidean.hdf5 /test -s 12 -k 100 -t
+[Timing]: knnsearch: 5103 [msec]
+
+# CILK-mnist over threads
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 7744 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 2 -k 100 -t
+[Timing]: knnsearch: 7206 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -k 100 -t
+[Timing]: knnsearch: 6965 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 6 -k 100 -t
+[Timing]: knnsearch: 6628 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 8 -k 100 -t
+[Timing]: knnsearch: 7362 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 10 -k 100 -t
+[Timing]: knnsearch: 7813 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_cilk -c mtx/mnist-784-euclidean.hdf5 /test -s 12 -k 100 -t
+[Timing]: knnsearch: 158123[msec]
+
+# Pthreads-sift over threads
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 4254 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 2 -k 100 -t
+[Timing]: knnsearch: 2155 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 4 -k 100 -t
+[Timing]: knnsearch: 1133 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 6 -k 100 -t
+[Timing]: knnsearch: 877 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 8 -k 100 -t
+[Timing]: knnsearch: 724 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 10 -k 100 -t
+[Timing]: knnsearch: 640 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/sift-128-euclidean.hdf5 /test -s 12 -k 100 -t
+[Timing]: knnsearch: 682 [msec]
+
+# Pthreads-mnist over threads
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -k 100 -t
+[Timing]: knnsearch: 7889 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 2 -k 100 -t
+[Timing]: knnsearch: 3963 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 4 -k 100 -t
+[Timing]: knnsearch: 2058 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 6 -k 100 -t
+[Timing]: knnsearch: 1445 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 8 -k 100 -t
+[Timing]: knnsearch: 1496 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 10 -k 100 -t
+[Timing]: knnsearch: 1379 [msec]
+hoo2@shirka:~/Work/AUTH/PDS/homework_1$ eval $DOCK ./out/knnsearch_v1_pth -c mtx/mnist-784-euclidean.hdf5 /test -s 12 -k 100 -t
+[Timing]: knnsearch: 1352 [msec]
+

homework_1/run_measurements.sh

@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+#
+# Take measurements
+#
+
+DOCK="docker run --rm -v /home/hoo2/Work/AUTH/PDS/homework_1:/usr/src/PDS_homework_1 -w /usr/src/PDS_homework_1/ hoo2/hpcimage"
+
+echo " "
+echo "Serial"
+
+     echo "./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -k 100 -t"
+eval $DOCK ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 10 -k 100 -t
+     echo "./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -k 100 -t"
+eval $DOCK ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 10 -k 100 -t
+
+echo " "
+echo "OMP"
+
+#      echo ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -k 100 -t
+#eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 10 -k 100 -t
+#      echo ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -k 100 -t
+#eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 10 -k 100 -t
+#      echo ./out/knnsearch_v1 -c mtx/sift-128-euclidean.hdf5 /test -s 1 -k 100 -t
+#eval $DOCK ./out/knnsearch_v1_omp -c mtx/sift-128-euclidean.hdf5 /test -s 10 -k 100 -t
+#      echo ./out/knnsearch_v1 -c mtx/mnist-784-euclidean.hdf5 /test -s 1 -k 100 -t
+#eval $DOCK ./out/knnsearch_v1_omp -c mtx/mnist-784-euclidean.hdf5 /test -s 10 -k 100 -t

homework_1/src/main.cpp

@@ -65,9 +65,15 @@ bool get_options(int argc, char* argv[]){
       else if (arg == "-k") {
          session.k = (i+1 < argc) ? std::atoi(argv[++i]) : session.k;
       }
-      else if (arg == "-n" || arg == "--max_trheads") {
+      else if (arg == "-n" || arg == "--max_threads") {
          session.max_threads = (i+1 < argc) ? std::atoi(argv[++i]) : session.max_threads;
       }
+      else if (arg == "-s" || arg == "--slices") {
+         session.slices = (i+1 < argc) ? std::atoi(argv[++i]) : session.slices;
+      }
+      else if (arg == "-a" || arg == "--accuracy") {
+         session.accuracy = (i+1 < argc) ? std::atoi(argv[++i]) : session.accuracy;
+      }
       else if (arg == "-t" || arg == "--timing")
          session.timing = true;
       else if (arg == "-v" || arg == "--verbose")
@@ -87,7 +93,12 @@ bool get_options(int argc, char* argv[]){
          std::cout << "   -k <number>\n";
          std::cout << "      Set the number of closest neighbors to find. \n\n";
          std::cout << "   -n | --max_trheads <threads>\n";
-         std::cout << "      Reduce the thread number for the execution to <threads>. <threads> must be less or equal to available CPUs.\n\n";
+         std::cout << "      Reduce the thread number for the execution to <threads>. <threads> should be less or equal to available CPUs.\n\n";
+         std::cout << "   -s | --slices <slices/threads>\n";
+         std::cout << "      The number of slices to the Corpus matrix. In the parallel version this setting affects the number of threads\n";
+         std::cout << "      <threads> should be less or equal to available CPUs\n\n";
+         std::cout << "   -a | --accuracy <accuracy>\n";
+         std::cout << "      Reduce the accuracy of neighbor finding. The accuracy should be between 1-100 \n\n";
          std::cout << "   -t | --timing\n";
          std::cout << "      Request timing measurements output to stdout.\n\n";
          std::cout << "   -v | --verbose\n";
@@ -109,6 +120,27 @@ bool get_options(int argc, char* argv[]){
    return status;
 }
 
+void loadMtx(MatrixDst& Corpus, MatrixDst& Query) {
+   if (access(session.outMtxFile.c_str(), F_OK) == 0)
+      std::remove(session.outMtxFile.c_str());
+
+//   timer.start();
+   Mtx::load<MatrixDst, DstHDF5Type>(session.corpusMtxFile, session.corpusDataSet, Corpus);
+   if (session.queryMtx)
+      Mtx::load<MatrixDst, DstHDF5Type>(session.corpusMtxFile, session.corpusDataSet, Query);
+//   timer.stop();
+//   timer.print_dt("Load hdf5 files");
+}
+
+void storeMtx(MatrixIdx& Idx, MatrixDst& Dst) {
+//   timer.start();
+   Mtx::store<MatrixIdx, IdxHDF5Type>(session.outMtxFile, session.outMtxIdxDataSet, Idx);
+   Mtx::store<MatrixDst, DstHDF5Type>(session.outMtxFile, session.outMtxDstDataSet, Dst);
+//   timer.stop();
+//   timer.print_dt("Store hdf5 files");
+}
+
+
 #ifndef TESTING
 int main(int argc, char* argv[]) try {
    // Instantiate matrixes
@@ -127,38 +159,26 @@ int main(int argc, char* argv[]) try {
    if (!get_options(argc, argv))
       exit(1);
 
-   if (access(session.outMtxFile.c_str(), F_OK) == 0)
-      std::remove(session.outMtxFile.c_str());
+   init_workers();
 
    // Load data
-   timer.start();
-   Mtx::load<MatrixDst, DstHDF5Type>(session.corpusMtxFile, session.corpusDataSet, Corpus);
-   if (session.queryMtx)
-      Mtx::load<MatrixDst, DstHDF5Type>(session.corpusMtxFile, session.corpusDataSet, Query);
-   timer.stop();
-   timer.print_dt("Load hdf5 files");
+   loadMtx(Corpus, Query);
 
    // Prepare output memory
-   Idx.resize(Query.rows(), session.k);
-   Dst.resize(Query.rows(), session.k);
+   Idx.resize((session.queryMtx) ? Query.rows() : Corpus.rows(), session.k);
+   Dst.resize((session.queryMtx) ? Query.rows() : Corpus.rows(), session.k);
 
    // Do the search
    logger << "Start knnsearch ...";
    timer.start();
-   if (session.queryMtx)
-      knnsearch(Corpus, Query, 0, session.k, session.k, Idx, Dst);
-   else
-      knnsearch(Corpus, Corpus, 0, session.k, session.k, Idx, Dst);
+   size_t selected_neighbors = (size_t)(session.k*(session.accuracy/100.0));
+   knnsearch(Corpus, (session.queryMtx) ? Query : Corpus, session.slices, session.k, selected_neighbors, Idx, Dst);
    timer.stop();
    logger << " Done" << logger.endl;
    timer.print_dt("knnsearch");
 
    // Store data
-   timer.start();
-   Mtx::store<MatrixIdx, IdxHDF5Type>(session.outMtxFile, session.outMtxIdxDataSet, Idx);
-   Mtx::store<MatrixDst, DstHDF5Type>(session.outMtxFile, session.outMtxDstDataSet, Dst);
-   timer.stop();
-   timer.print_dt("Store hdf5 files");
+   storeMtx(Idx, Dst);
 
    return 0;
 }

homework_1/src/v1.cpp

@@ -0,0 +1,50 @@
+/**
+ * \file    v1.hpp
+ * \brief
+ *
+ * \author
+ *    Christos Choutouridis AEM:8997
+ *    <cchoutou@ece.auth.gr>
+ */
+
+#include "v1.hpp"
+
+
+void init_workers() {
+#if defined CILK
+   size_t cilk_w = __cilkrts_get_nworkers();
+
+   if (!session.max_threads)
+      session.max_threads = (session.slices) ? (session.slices) : cilk_w;
+//   else if (session.max_threads < cilk_w)
+//      __cilkrts_set_param("nworkers", "4");
+   // else ignored by cilk
+
+#elif defined OMP
+//   omp_set_dynamic(1);
+   size_t omp_w = (size_t)omp_get_max_threads();
+
+   if (!session.max_threads) {
+      session.max_threads = (session.slices) ? (session.slices) : omp_w;
+//      omp_set_dynamic(1);
+   }
+   else if (session.max_threads < omp_w) {
+//      omp_set_dynamic(0);
+      omp_set_num_threads(session.max_threads);
+   }
+   // else ignored by omp
+
+#elif defined PTHREADS
+   size_t pth_w = std::thread::hardware_concurrency();
+
+   if (!session.max_threads)
+      session.max_threads = (session.slices) ? (session.slices) : pth_w;
+#else
+
+#endif
+   if (!session.slices)
+      session.slices = session.max_threads;
+
+   openblas_set_num_threads(1); // Limit OpenBLAS to 1 thread
+}
+

homework_1/test/ReadME.md

@@ -0,0 +1,9 @@
+## NOTE ##
+This folder contains the output files from tests.
+
+In order to populate it, run the tests
+
+$> make tests
+$> ./tests
+
+