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HW2: (WIP) Checkpoint with code re-arrangement and elbowSort

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This commit is contained in:
Christos Choutouridis 2024-12-30 14:23:13 +02:00
parent 1d6b271d2a
commit 4d1d7502aa
11 changed files with 545 additions and 1193 deletions
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7
homework_2/include/config.h
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@ -10,8 +10,7 @@
#ifndef CONFIG_H_
#define CONFIG_H_
#include <iostream>
#include <string>
#include <cstdint>
/*
* Defines for different version of the exercise
@ -25,11 +24,15 @@
#define CODE_VERSION BITONIC
#endif
// Value type selection
using distValue_t = uint8_t;
/*!
* Session option for each invocation of the executable
*/
struct session_t {
size_t arraySize{0};
bool ndebug{false};
bool timing{false};
bool verbose{false}; //!< Flag to enable verbose output to stdout
};

73
homework_2/include/distbitonic.hpp
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@ -1,73 +0,0 @@
/*!
* \file
* \brief Distributed bitonic implementation header
*
* \author
* Christos Choutouridis AEM:8997
* <cchoutou@ece.auth.gr>
*/
#ifndef DISTBITONIC_H_
#define DISTBITONIC_H_
#include <cstdint>
#include "utils.hpp"
/*!
* Enumerator for the different versions of the sorting method
*/
enum class SortMode {
Bubbletonic, //!< The v0.5 of the algorithm where we use a bubble-sort like approach
Bitonic //!< The v1.0 of the algorithm where we use the bitonic data-exchange approach
};
using Data_t = std::vector<uint8_t>;
using AllData_t = std::vector<Data_t>;
/*
* ============================== Sort utilities ==============================
*/
/*!
* The primary function template of ascending(). It is DISABLED since , it is explicitly specialized
* for each of the \c SortMode
*/
template <SortMode Mode> bool ascending(mpi_id_t, [[maybe_unused]] size_t) noexcept = delete;
template <> bool ascending<SortMode::Bubbletonic>(mpi_id_t node, [[maybe_unused]] size_t depth) noexcept;
template <> bool ascending<SortMode::Bitonic>(mpi_id_t node, size_t depth) noexcept;
/*!
* The primary function template of partner(). It is DISABLED since , it is explicitly specialized
* for each of the \c SortMode
*/
template <SortMode Mode> mpi_id_t partner(mpi_id_t, size_t) noexcept = delete;
template <> mpi_id_t partner<SortMode::Bubbletonic>(mpi_id_t node, size_t step) noexcept;
template <> mpi_id_t partner<SortMode::Bitonic>(mpi_id_t node, size_t step) noexcept;
/*!
* The primary function template of keepSmall(). It is DISABLED since , it is explicitly specialized
* for each of the \c SortMode
*/
template<SortMode Mode> bool keepSmall(mpi_id_t, mpi_id_t, [[maybe_unused]] size_t) noexcept = delete;
template<> bool keepSmall<SortMode::Bubbletonic>(mpi_id_t node, mpi_id_t partner, [[maybe_unused]] size_t depth) noexcept;
template<> bool keepSmall<SortMode::Bitonic>(mpi_id_t node, mpi_id_t partner, size_t depth) noexcept;
bool isActive(mpi_id_t node, mpi_id_t nodes) noexcept;
/*
* ============================== Data utilities ==============================
*/
void exchange(mpi_id_t node, mpi_id_t partner);
void minmax(AllData_t& data, mpi_id_t node, mpi_id_t partner, bool keepsmall);
/*
* ============================== Sort algorithms ==============================
*/
void bubbletonic_network(AllData_t& data, mpi_id_t nodes);
void distBubbletonic(mpi_id_t P, AllData_t& data);
void bitonic_network(AllData_t& data, mpi_id_t nodes, mpi_id_t depth);
void distBitonic(mpi_id_t P, AllData_t& data);
#endif //DISTBITONIC_H_

288
homework_2/include/distsort.hpp Normal file
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/*!
* \file
* \brief Distributed sort implementation header
*
* \author
* Christos Choutouridis AEM:8997
* <cchoutou@ece.auth.gr>
*/
#ifndef DISTBITONIC_H_
#define DISTBITONIC_H_
#include <vector>
#include <algorithm>
#include <cmath>
#include <cstdint>
#if !defined DEBUG
#define NDEBUG
#endif
#include <cassert>
#include "utils.hpp"
#include "config.h"
/*!
* Enumerator for the different versions of the sorting method
*/
enum class SortMode {
Bubbletonic, //!< The v0.5 of the algorithm where we use a bubble-sort like approach
Bitonic //!< The v1.0 of the algorithm where we use the bitonic data-exchange approach
};
/*
* ============================== Sort utilities ==============================
*/
/*!
* The primary function template of ascending(). It is DISABLED since , it is explicitly specialized
* for each of the \c SortMode
*/
template <SortMode Mode> inline bool ascending(mpi_id_t, [[maybe_unused]] size_t) noexcept = delete;
/*!
* Returns the ascending or descending configuration of the node's sequence based on
* the current node (MPI process) and the depth of the sorting network
*
* @param node The current node (MPI process)
* @return True if we need ascending configuration, false otherwise
*/
template <> inline
bool ascending<SortMode::Bubbletonic>(mpi_id_t node, [[maybe_unused]] size_t depth) noexcept {
return (node % 2) == 0;
}
/*!
* Returns the ascending or descending configuration of the node's sequence based on
* the current node (MPI process) and the depth of the sorting network
*
* @param node The current node (MPI process)
* @param depth The total depth of the sorting network (same for each step for a given network)
*
* @return True if we need ascending configuration, false otherwise
*/
template <> inline
bool ascending<SortMode::Bitonic>(mpi_id_t node, size_t depth) noexcept {
return !(node & (1 << depth));
}
/*!
* The primary function template of partner(). It is DISABLED since , it is explicitly specialized
* for each of the \c SortMode
*/
template <SortMode Mode> inline mpi_id_t partner(mpi_id_t, size_t) noexcept = delete;
/*!
* Returns the node's partner for data exchange during the sorting network iterations
* of Bubbletonic
*
* @param node The current node
* @param step The step of the sorting network
* @return The node id of the partner for data exchange
*/
template <> inline
mpi_id_t partner<SortMode::Bubbletonic>(mpi_id_t node, size_t step) noexcept {
//return (node % 2 == step % 2) ? node + 1 : node - 1;
return (((node+step) % 2) == 0) ? node + 1 : node - 1;
}
/*!
* Returns the node's partner for data exchange during the sorting network iterations
* of Bitonic
*
* @param node The current node
* @param step The step of the sorting network
* @return The node id of the partner for data exchange
*/
template <> inline
mpi_id_t partner<SortMode::Bitonic>(mpi_id_t node, size_t step) noexcept {
return (node ^ (1 << step));
}
/*!
* The primary function template of keepSmall(). It is DISABLED since , it is explicitly specialized
* for each of the \c SortMode
*/
template<SortMode Mode> inline bool keepSmall(mpi_id_t, mpi_id_t, [[maybe_unused]] size_t) noexcept = delete;
/*!
* Predicate to check if a node keeps the small numbers during the bubbletonic sort network exchange.
*
* @param node The node for which we check
* @param partner The partner of the data exchange
* @return True if the node should keep the small values, false otherwise
*/
template <> inline
bool keepSmall<SortMode::Bubbletonic>(mpi_id_t node, mpi_id_t partner, [[maybe_unused]] size_t depth) noexcept {
assert(node != partner);
return (node < partner);
}
/*!
* Predicate to check if a node keeps the small numbers during the bitonic sort network exchange.
*
* @param node The node for which we check
* @param partner The partner of the data exchange
* @param depth The total depth of the sorting network (same for each step for a given network)
* @return True if the node should keep the small values, false otherwise
*/
template <> inline
bool keepSmall<SortMode::Bitonic>(mpi_id_t node, mpi_id_t partner, size_t depth) noexcept {
assert(node != partner);
return ascending<SortMode::Bitonic>(node, depth) == (node < partner);
}
/*!
* Predicate to check if the node is active in the current iteration of the bubbletonic
* sort exchange.
*
* @param node The node to check
* @param nodes The total number of nodes
* @return True if the node is active, false otherwise
*/
bool isActive(mpi_id_t node, size_t nodes) noexcept;
/*
* ============================== Data utilities ==============================
*/
/*!
*
* @tparam RangeT
* @param data
* @param ascending
*/
template<typename RangeT>
void fullSort(RangeT& data, bool ascending) {
// Use introsort from stdlib++ here, unless ...
if (ascending)
std::sort(data.begin(), data.end(), std::less<>());
else
std::sort(data.begin(), data.end(), std::greater<>());
}
/*!
*
* @tparam ShadowedT
* @tparam CompT
* @param data
* @param comp
*/
template<typename ShadowedT, typename CompT>
void elbowSortCore(ShadowedT& data, CompT comp) {
size_t N = data.size();
auto active = data.getActive();
auto shadow = data.getShadow();
size_t left = std::distance(
active.begin(),
std::min_element(active.begin(), active.end())
);
size_t right = (left == N-1) ? 0 : left + 1;
for (size_t i = 0 ; i<N ; ++i) {
if (comp(active[left], active[right])) {
shadow[i] = active[left];
left = (left == 0) ? N-1 : left -1;
}
else {
shadow[i] = active[right];
right = (right + 1) % N;
}
}
data.switch_active();
}
/*!
*
* @tparam ShadowedT
* @param data
* @param ascending
*/
template<typename ShadowedT>
void elbowSort(ShadowedT& data, bool ascending) {
if (ascending)
elbowSortCore(data, std::less<>());
else
elbowSortCore(data, std::greater<>());
}
/*!
*
* @tparam RangeT
* @param local
* @param remote
* @param keepsmall
*/
template<typename RangeT>
void minmax(RangeT& local, RangeT& remote, bool keepsmall) {
using value_t = typename RangeT::value_type;
std::transform(
local.begin(), local.end(),
remote.begin(),
local.begin(),
[keepsmall](const value_t& a, const value_t& b){
return (keepsmall) ? std::min(a, b) : std::max(a, b);
});
}
/*
* ============================== Sort algorithms ==============================
*/
/*!
*
* @tparam ShadowedT
* @param data
* @param Processes
*/
template<typename ShadowedT>
void distBubbletonic(ShadowedT& data, mpi_id_t Processes) {
// Initially sort to create a half part of a bitonic sequence
fullSort(data, ascending<SortMode::Bubbletonic>(mpi.rank(), 0));
// Sort network
for (size_t step = 0; step < Processes-1; ++step) {
auto part = partner<SortMode::Bubbletonic>(mpi.rank(), step);
auto ks = keepSmall<SortMode::Bubbletonic>(mpi.rank(), part, Processes);
if (isActive(mpi.rank(), Processes)) {
mpi.exchange(part, data.getActive(), data.getShadow(), step);
minmax(data.getActive(), data.getShadow(), ks);
elbowSort(data, ascending<SortMode::Bubbletonic>(mpi.rank(), Processes));
}
}
if (!ascending<SortMode::Bubbletonic>(mpi.rank(), 0))
elbowSort(data, true);
}
/*!
*
* @tparam ShadowedT
* @param data
* @param Processes
*/
template<typename ShadowedT>
void distBitonic(ShadowedT& data, mpi_id_t Processes) {
auto p = static_cast<uint32_t>(std::log2(Processes));
// Initially sort to create a half part of a bitonic sequence
fullSort(data, ascending<SortMode::Bitonic>(mpi.rank(), 0));
// Run through sort network using elbow-sort
for (size_t depth = 1; depth <= p; ++depth) {
for (size_t step = depth; step > 0;) {
--step;
auto part = partner<SortMode::Bitonic>(mpi.rank(), step);
auto ks = keepSmall<SortMode::Bitonic>(mpi.rank(), part, depth);
mpi.exchange(part, data.getActive(), data.getShadow(), (depth << 8) | step);
minmax(data.getActive(), data.getShadow(), ks);
}
elbowSort (data, ascending<SortMode::Bitonic>(mpi.rank(), depth));
}
}
#endif //DISTBITONIC_H_

14
homework_2/include/impl.hpp
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@ -1,14 +0,0 @@
/*!
* \file
* \brief The distributed bitonic implementation header
*
* \author
* Christos Choutouridis AEM:8997
* <cchoutou@ece.auth.gr>
*/
#ifndef IMPL_H_
#define IMPL_H_
#endif //IMPL_H_

804
homework_2/include/matrix.hpp
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/**
* \file matrix.hpp
* \brief A matrix abstraction implementation
*
* \author
* Christos Choutouridis AEM:8997
* <cchoutou@ece.auth.gr>
*/
#ifndef MATRIX_HPP_
#define MATRIX_HPP_
#include <type_traits>
#include <utility>
#include <algorithm>
#include <vector>
#include <tuple>
namespace mtx {
using std::size_t;
/*
* Small helper to strip types
*/
template<typename T>
struct remove_cvref {
typedef std::remove_cv_t<std::remove_reference_t<T>> type;
};
template<typename T>
using remove_cvref_t = typename remove_cvref<T>::type;
/*!
* Enumerator to denote the storage type of the array to use.
*/
enum class MatrixType {
DENSE, /*!< Matrix is dense */
SPARSE, /*!< Matrix is sparse */
};
/*!
* Enumerator to denote the storage type of the array to use.
*/
enum class MatrixOrder {
COLMAJOR, /*!< Matrix is column major */
ROWMAJOR, /*!< Matrix is row major */
};
/*
* Forward type declarations
*/
template<typename MatrixType> struct MatCol;
template<typename MatrixType> struct MatRow;
template<typename MatrixType> struct MatVal;
/*!
* A 2-D matrix functionality over a 1-D array
*
* This is a very thin abstraction layer over a native array.
* This is tested using compiler explorer and our template produce
* almost identical assembly.
*
* The penalty hit we have is due to the fact that we use a one dimension array
* and we have to calculate the actual position from an (i,j) pair.
* The use of 1D array was our intention from the beginning, so the penalty
* was pretty much unavoidable.
*
* \tparam DataType The underling data type of the array
* \tparam IndexType The underling type for the index variables and sizes
* \tparam Type The storage type of the array
* \arg FULL For full matrix
* \arg SYMMETRIC For symmetric matrix (we use only the lower part)
*/
template<typename DataType,
typename IndexType = size_t,
MatrixType Type = MatrixType::DENSE,
MatrixOrder Order = MatrixOrder::ROWMAJOR,
bool Symmetric = false>
struct Matrix {
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
static constexpr bool symmetric = Symmetric; //!< meta:export symmetric flag
/*!
* \name Obj lifetime
*/
//! @{
//! Construct an empty matrix with dimensions rows x columns
Matrix(IndexType rows = IndexType{}, IndexType columns = IndexType{}) noexcept
: vector_storage_(capacity(rows, columns)),
raw_storage_(nullptr),
use_vector_(true),
rows_(rows),
cols_(columns) {
data_ = vector_storage_.data();
}
//! Construct a matrix by copying existing data with dimensions rows x columns
Matrix(DataType* data, IndexType major_start, IndexType major_length, IndexType minor_length) noexcept
: vector_storage_(),
raw_storage_ (data + major_start * minor_length),
use_vector_ (false) {
if constexpr (Order == MatrixOrder::ROWMAJOR) {
rows_ = major_length;
cols_ = minor_length;
}
else {
rows_ = minor_length;
cols_ = major_length;
}
data_ = raw_storage_;
}
//! Construct a matrix using an initializer list
Matrix(IndexType rows, IndexType columns, std::initializer_list<DataType> list)
: vector_storage_(list),
raw_storage_(nullptr),
use_vector_(true),
rows_(rows),
cols_(columns) {
if (list.size() != capacity(rows, columns)) {
throw std::invalid_argument("Matrix initializer list size does not match matrix dimensions.");
}
data_ = vector_storage_.data();
}
//! move ctor
Matrix(Matrix&& m) noexcept { moves(std::move(m)); }
//! move
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
//! @{
//! Get/Set the size of each dimension
IndexType rows() const noexcept { return rows_; }
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_;
}
//! Set the interface size of the Matrix (what appears to be the size)
IndexType resize(IndexType rows, IndexType columns) {
if (use_vector_) {
rows_ = rows;
cols_ = columns;
vector_storage_.resize(capacity(rows_, cols_));
data_ = vector_storage_.data();
}
return capacity(rows_, cols_);
}
//! Actual memory capacity of the symmetric matrix
static constexpr IndexType capacity(IndexType M, IndexType N) {
if constexpr (Symmetric)
return (M+1)*N/2;
else
return M*N;
}
/*
* virtual 2D accessors
*/
DataType get (IndexType i, IndexType j) {
if constexpr (Symmetric) {
auto T = [](size_t i)->size_t { return i*(i+1)/2; }; // Triangular number of i
if constexpr (Order == MatrixOrder::COLMAJOR) {
// In column major we use the lower triangle of the matrix
if (i>=j) return data_[j*rows_ - T(j) + i]; // Lower, use our notation
else return data_[i*rows_ - T(i) + j]; // Upper, use opposite index
}
else {
// In row major we use the upper triangle of the matrix
if (i<=j) return data_[i*cols_ - T(i) + j]; // Upper, use our notation
else return data_[j*cols_ - T(j) + i]; // Lower, use opposite index
}
}
else {
if constexpr (Order == MatrixOrder::COLMAJOR)
return data_[i + j*rows_];
else
return data_[i*cols_ + j];
}
}
/*!
* \fn DataType set(DataType, IndexType, IndexType)
* \param v
* \param i
* \param j
* \return
*/
DataType set (DataType v, IndexType i, IndexType j) {
if constexpr (Symmetric) {
auto T = [](size_t i)->size_t { return i*(i+1)/2; }; // Triangular number of i
if constexpr (Order == MatrixOrder::COLMAJOR) {
// In column major we use the lower triangle of the matrix
if (i>=j) return data_[j*rows_ - T(j) + i] = v; // Lower, use our notation
else return data_[i*rows_ - T(i) + j] = v; // Upper, use opposite index
}
else {
// In row major we use the upper triangle of the matrix
if (i<=j) return data_[i*cols_ - T(i) + j] = v; // Upper, use our notation
else return data_[j*cols_ - T(j) + i] = v; // Lower, use opposite index
}
}
else {
if constexpr (Order == MatrixOrder::COLMAJOR)
return data_[i + j*rows_] = v;
else
return data_[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> operator()(IndexType i, IndexType j) noexcept {
return MatVal<Matrix>(this, get(i, j), i, j);
}
// 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_); }
const DataType* data() const noexcept { return data_; }
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);
}
}
//! @}
//
void swap(Matrix& src) noexcept {
std::swap(vector_storage_, src.vector_storage_);
std::swap(raw_storage_, src.raw_storage_);
std::swap(data_, src.data_);
std::swap(use_vector_, src.use_vector_);
std::swap(rows_, src.rows_);
std::swap(cols_, src.cols_);
}
private:
//! move helper
void moves(Matrix&& src) noexcept {
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).
DataType* data_; //!< Pointer to active storage.
bool use_vector_; //!< True if using vector storage, false for raw pointer.
IndexType rows_{}; //!< the virtual size of rows.
IndexType cols_{}; //!< the virtual size of columns.
};
/**
* A simple sparse matrix specialization.
*
* We use CSC format and provide get/set functionalities for each (i,j) item
* on the matrix. We also provide a () overload using a proxy MatVal object.
* This way the user can:
* \code
* auto v = A(3,4);
* A(3, 4) = 7;
* \endcode
*
* We also provide getCol() and getRow() functions witch return a viewer/iterator to rows and
* columns of the matrix. In the case of a symmetric matrix instead of a row we return the
* equivalent column. This way we gain speed due to CSC format nature.
*
* @tparam DataType The type for values
* @tparam IndexType The type for indexes
* @tparam Type The Matrix type (FULL or SYMMETRIC)
*/
template<typename DataType, typename IndexType,
MatrixOrder Order,
bool Symmetric>
struct Matrix<DataType, IndexType, MatrixType::SPARSE, Order, Symmetric> {
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 = MatrixType::SPARSE; //!< meta:export of array type
static constexpr bool symmetric = Symmetric; //!< meta:export symmetric flag
friend struct MatCol<Matrix>;
friend struct MatRow<Matrix>;
friend struct MatVal<Matrix>;
/*!
* \name Obj lifetime
*/
//! @{
//! Default ctor with optional memory allocations
Matrix(IndexType n=IndexType{}) noexcept:
values{},
rows{},
col_ptr((n)? n+1:2, IndexType{}),
N(n),
NNZ(0) { }
//! A ctor using csc array data
Matrix(IndexType n, IndexType nnz, const IndexType* row, const IndexType* col) noexcept:
values(nnz, 1),
rows(row, row+nnz),
col_ptr(col, col+n+1),
N(n),
NNZ(nnz) { }
//! ctor using csc array data with value array
Matrix(IndexType n, IndexType nnz, const DataType* v, const IndexType* row, const IndexType* col) noexcept:
values(v, v+nnz),
rows(row, row+nnz),
col_ptr(col, col+n+1),
N(n),
NNZ(nnz) { }
//! ctor vectors of row/col and default value for values array
Matrix(IndexType n, IndexType nnz, const DataType v,
const std::vector<IndexType>& row, const std::vector<IndexType>& col) noexcept:
values(nnz, v),
rows (row),
col_ptr(col),
N(n),
NNZ(nnz) { }
//! move ctor
Matrix(Matrix&& m) noexcept { moves(std::move(m)); }
//! move
Matrix& operator=(Matrix&& m) noexcept { moves(std::move(m)); return *this; }
Matrix(const Matrix& m) = delete; //!< make sure there are no copies
Matrix& operator=(const Matrix& m) = delete; //!< make sure there are no copies
//! @}
//! \name Data exposure
//! @{
//! \return the dimension of the matrix
IndexType size() noexcept { return N; }
//! After construction size configuration tool
IndexType resize(IndexType n) {
col_ptr.resize(n+1);
return N = n;
}
//! \return the NNZ of the matrix
IndexType capacity() noexcept { return NNZ; }
//! After construction NNZ size configuration tool
IndexType capacity(IndexType nnz) noexcept {
values.reserve(nnz);
rows.reserve(nnz);
return NNZ;
}
// getters for row arrays of the struct (unused)
std::vector<DataType>& getValues() noexcept { return values; }
std::vector<IndexType>& getRows() noexcept { return rows; }
std::vector<IndexType>& getCols() noexcept { return col_ptr; }
/*!
* 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> operator()(IndexType i, IndexType j) noexcept {
return MatVal<Matrix>(this, get(i, j), i, j);
}
/*!
* A read item functionality using binary search to find the correct row
*
* @param i The row number
* @param j The column number
* @return The value of the item or DataType{} if is not present.
*/
DataType get(IndexType i, IndexType j) noexcept {
IndexType idx; bool found;
std::tie(idx, found) =find_idx(rows, col_ptr[j], col_ptr[j+1], i);
return (found) ? values[idx] : 0;
}
/*!
* A write item functionality.
*
* First we search if the matrix has already a value in (i, j) position.
* If so we just change it to a new value. If not we add the item on the matrix.
*
* @note
* When change a value, we don't increase the NNZ value of the struct. We expect the user has already
* change the NNZ value to the right one using @see capacity() function. When adding a value we
* increase the NNZ.
*
* @param i The row number
* @param j The column number
* @return The new value of the item .
*/
DataType set(DataType v, IndexType i, IndexType j) {
IndexType idx; bool found;
std::tie(idx, found) = find_idx(rows, col_ptr[j], col_ptr[j+1], i);
if (found)
return values[idx] = v; // we don't change NNZ even if we write "0"
else {
values.insert(values.begin()+idx, v);
rows.insert(rows.begin()+idx, i);
std::transform(col_ptr.begin()+j+1, col_ptr.end(), col_ptr.begin()+j+1, [](IndexType it) {
return ++it;
});
++NNZ; // we increase the NNZ even if we write "0"
return v;
}
}
/*!
* Get a view of a CSC column
* @param j The column to get
* @return The MatCol object @see MatCol
*/
MatCol<Matrix> getCol(IndexType j) noexcept {
return MatCol<Matrix>(this, col_ptr[j], col_ptr[j+1]);
}
/*!
* Get a view of a CSC row
*
* In case of a SYMMETRIC matrix we can return a column instead.
*
* @param j The row to get
* @return On symmetric matrix MatCol otherwise a MatRow
*/
MatCol<Matrix> getRow(IndexType i) noexcept {
if constexpr (Symmetric)
return getCol(i);
else
return MatRow<Matrix>(this, i);
}
// values only iterator support
DataType* begin() noexcept { return values.begin(); }
DataType* end() noexcept { return values.end(); }
//! @}
//! A small iteration helper
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:
/*!
* A small binary search implementation using index for begin-end instead of iterators.
*
* \param v Reference to vector to search
* \param begin The vector's index to begin
* \param end The vector's index to end
* \param match What to search
* \return An <index, status> pair.
* index is the index of the item or end if not found
* status is true if found, false otherwise
*/
std::pair<IndexType, bool> find_idx(const std::vector<IndexType>& v, IndexType begin, IndexType end, IndexType match) {
if (v.capacity() != 0 && begin < end) {
IndexType b = begin, e = end-1;
while (b <= e) {
IndexType m = (b+e)/2;
if (v[m] == match) return std::make_pair(m, true);
else if (b >= e) return std::make_pair(end, false);
else {
if (v[m] < match) b = m +1;
else e = m -1;
}
}
}
return std::make_pair(end, false);
}
// move helper
void moves(Matrix&& src) noexcept {
values = std::move(src.values);
rows = std::move(src.rows);
col_ptr = std::move(src.col_ptr);
N = std::move(src.N); // redundant for primitives
NNZ = std::move(src.NNZ); //
}
//! \name Data
//! @{
std::vector<DataType> values {}; //!< vector to store the values of the matrix
std::vector<IndexType> rows{}; //!< vector to store the row information
std::vector<IndexType> col_ptr{1,0}; //!< vector to store the column pointers
IndexType N{0}; //!< The dimension of the matrix (square)
IndexType NNZ{0}; //!< The NNZ (capacity of the matrix)
//! @}
};
/*!
* A view/iterator hybrid object for Matrix columns.
*
* This object provides access to a column of a Matrix. The public functionalities
* allow data access using indexes instead of iterators. We prefer indexes over iterators
* because we can apply the same index to different inner vector of Matrix without conversion.
*
* @tparam DataType
* @tparam IndexType
*/
template<typename MatrixType>
struct MatCol {
using owner_t = MatrixType;
using DataType = typename MatrixType::dataType;
using IndexType = typename MatrixType::indexType;
/*!
* ctor using column pointers for begin-end. own is pointer to Matrix.
*/
MatCol(owner_t* own, const IndexType begin, const IndexType end) noexcept :
owner_(own), index_(begin), begin_(begin), end_(end) {
vindex_ = vIndexCalc(index_);
}
MatCol() = default;
MatCol(const MatCol&) = delete; //!< make sure there are no copies
MatCol& operator=(const MatCol&)= delete; //!< make sure there are no copies
MatCol(MatCol&&) = default;
MatCol& operator=(MatCol&&) = default;
//! a simple dereference operator, like an iterator
DataType operator* () {
return get();
}
//! Increment operator acts on index(), like an iterator
MatCol& operator++ () { advance(); return *this; }
MatCol& operator++ (int) { MatCol& p = *this; advance(); return p; }
//! () operator acts as member access (like a view)
DataType operator()(IndexType x) {
return (x == index())? get() : DataType{};
}
//! = operator acts as member assignment (like a view)
DataType operator= (DataType v) { return owner_->values[index_] = v; }
// iterator like handlers
// these return a virtual index value based on the items position on the full matrix
// but the move of the index is just a ++ away.
IndexType index() noexcept { return vindex_; }
const IndexType index() const noexcept { return vindex_; }
IndexType begin() noexcept { return vIndexCalc(begin_); }
const IndexType begin() const noexcept { return vIndexCalc(begin_); }
IndexType end() noexcept { return owner_->N; }
const IndexType end() const noexcept { return owner_->N; }
/*!
* Multiplication operator
*
* We follow only the non-zero values and multiply only the common indexes.
*
* @tparam C Universal reference for the type right half site column
*
* @param c The right hand site matrix
* @return The value of the inner product of two vectors
* @note The time complexity is \$ O(nnz1+nnz2) \$.
* Where the nnz is the max NNZ elements of the column of the matrix
*/
template <typename C>
DataType operator* (C&& c) {
static_assert(std::is_same<remove_cvref_t<C>, MatCol<MatrixType>>(), "");
DataType v{};
while (index() != end() && c.index() != c.end()) {
if (index() < c.index()) advance(); // advance me
else if (index() > c.index()) ++c; // advance other
else { //index() == c.index()
v += get() * *c; // multiply and advance both
++c;
advance();
}
}
return v;
}
private:
//! small tool to increase the index pointers to Matrix
void advance() noexcept {
++index_;
vindex_ = vIndexCalc(index_);
}
//! tool to translate between col_ptr indexes and Matrix "virtual" full matrix indexes
IndexType vIndexCalc(IndexType idx) {
return (idx < end_) ? owner_->rows[idx] : end();
}
//! small get tool
DataType get() { return owner_->values[index_]; }
owner_t* owner_ {nullptr}; //!< Pointer to owner Matrix. MatCol is just a view
IndexType vindex_ {IndexType{}}; //!< Virtual index of full matrix
IndexType index_ {IndexType{}}; //!< index to Matrix::rows
IndexType begin_ {IndexType{}}; //!< beginning index of the column in Matrix::rows
IndexType end_ {IndexType{}}; //!< ending index of the column in Matrix::rows
};
/*!
* A view/iterator hybrid object for Matrix rows.
*
* This object provides access to a column of a Matrix. The public functionalities
* allow data access using indexes instead of iterators. We prefer indexes over iterators
* because we can apply the same index to different inner vector of Matrix without conversion.
*
* @tparam DataType
* @tparam IndexType
*/
template<typename MatrixType>
struct MatRow {
using owner_t = MatrixType;
using DataType = typename MatrixType::dataType;
using IndexType = typename MatrixType::indexType;
/*!
* ctor using virtual full matrix row index. own is pointer to Matrix.
*/
MatRow(owner_t* own, const IndexType row) noexcept :
owner_(own), vindex_(IndexType{}), row_(row), index_(IndexType{}),
begin_(IndexType{}), end_(owner_->NNZ) {
// place begin
while(begin_ != end_ && owner_->rows[begin_] != row_)
++begin_;
// place index_ and vindex_
if (owner_->rows[index_] != row_)
advance();
}
MatRow() = default;
MatRow(const MatRow&) = delete; //!< make sure there are no copies
MatRow& operator=(const MatRow&)= delete; //!< make sure there are no copies
MatRow(MatRow&&) = default;
MatRow& operator=(MatRow&&) = default;
//! a simple dereference operator, like an iterator
DataType operator* () {
return get();
}
//! Increment operator acts on index(), like an iterator
//! here the increment is a O(N) process.
MatRow& operator++ () { advance(); return *this; }
MatRow& operator++ (int) { MatRow& p = *this; advance(); return p; }
//! () operator acts as member access (like a view)
DataType operator()(IndexType x) {
return (x == index())? get() : DataType{};
}
//! = operator acts as member assignment (like a view)
DataType operator= (DataType v) { return owner_->values[index_] = v; }
// iterator like handlers
// these return a virtual index value based on the items position on the full matrix
// but the move of the index is just a ++ away.
IndexType index() noexcept { return vindex_; }
const IndexType index() const noexcept { return vindex_; }
IndexType begin() noexcept { return vIndexCalc(begin_); }
const IndexType begin() const noexcept { return vIndexCalc(begin_); }
IndexType end() noexcept { return owner_->N; }
const IndexType end() const noexcept { return owner_->N; }
/*!
* Multiplication operator
*
* We follow only the non-zero values and multiply only the common indexes.
*
* @tparam C Universal reference for the type right half site column
*
* @param c The right hand site matrix
* @return The value of the inner product of two vectors
* @note The time complexity is \$ O(N+nnz2) \$ and way heavier the ColxCol multiplication.
* Where the nnz is the max NNZ elements of the column of the matrix
*/
template <typename C>
DataType operator* (C&& c) {
static_assert(std::is_same<remove_cvref_t<C>, MatCol<MatrixType>>(), "");
DataType v{};
while (index() != end() && c.index() != c.end()) {
if (index() < c.index()) advance(); // advance me
else if (index() > c.index()) ++c; // advance other
else { //index() == c.index()
v += get() * *c; // multiply and advance both
++c;
advance();
}
}
return v;
}
private:
//! small tool to increase the index pointers to Matrix matrix
//! We have to search the entire rows vector in Matrix to find the next
//! virtual row position.
//! time complexity O(N)
void advance() noexcept {
do
++index_;
while(index_ != end_ && owner_->rows[index_] != row_);
vindex_ = vIndexCalc(index_);
}
//! tool to translate between col_ptr indexes and Matrix "virtual" full matrix indexes
IndexType vIndexCalc(IndexType idx) {
for(IndexType i =0 ; i<(owner_->N+1) ; ++i)
if (idx < owner_->col_ptr[i])
return i-1;
return end();
}
//! small get tool
DataType get() { return owner_->values[index_]; }
owner_t* owner_ {nullptr}; //!< Pointer to owner Matrix. MatCol is just a view
IndexType vindex_ {IndexType{}}; //!< Virtual index of full matrix
IndexType row_ {IndexType{}}; //!< The virtual full matrix row of the object
IndexType index_ {IndexType{}}; //!< index to Matrix::rows
IndexType begin_ {IndexType{}}; //!< beginning index of the column in Matrix::rows
IndexType end_ {IndexType{}}; //!< ending index of the column in Matrix::rows
};
/*!
* A proxy Matrix value object/view.
*
* This object acts as proxy to provide read/write access to an Matrix item.
*
* @tparam DataType The type of the values of the Matrix matrix
* @tparam IndexType The type of the indexes of the Matrix matrix
*/
template<typename MatrixType>
struct MatVal {
using owner_t = MatrixType;
using DataType = typename MatrixType::dataType;
using IndexType = typename MatrixType::indexType;
//!< ctor using all value-row-column data, plus a pointer to owner Matrix object
MatVal(owner_t* own, DataType v, IndexType i, IndexType j) :
owner_(own), v_(v), i_(i), j_(j) { }
MatVal() = default;
MatVal(const MatVal&) = delete; //!< make sure there are no copies
MatVal& operator=(const MatVal&) = delete; //!< make sure there are no copies
MatVal(MatVal&&) = default;
MatVal& operator=(MatVal&&) = default;
//! Operator to return the DataType value implicitly
operator DataType() { return v_; }
//! Operator to write back to owner the assigned value
//! for ex: A(2,3) = 5;
MatVal& operator=(DataType v) {
v_ = v;
owner_->set(v_, i_, j_);
return *this;
}
private:
owner_t* owner_{nullptr}; //!< Pointer to owner Matrix. MatVal is just a view.
DataType v_{DataType{}}; //!< The value of the row-column pair (for speed)
IndexType i_{IndexType{}}; //!< The row
IndexType j_{IndexType{}}; //!< the column
};
} // namespace mtx
#endif /* MATRIX_HPP_ */

121
homework_2/include/utils.hpp
View File

@ -1,5 +1,5 @@
/**
* \file utils.hpp
* \file
* \brief Utilities header
*
* \author
@ -9,6 +9,7 @@
#ifndef UTILS_HPP_
#define UTILS_HPP_
#include <vector>
#include <iostream>
#include <chrono>
#include <unistd.h>
@ -17,6 +18,18 @@
//#include "matrix.hpp"
#include "config.h"
template <typename T> struct MPI_TypeMapper;
// Specializations for supported types
template <> struct MPI_TypeMapper<char> { static MPI_Datatype getType() { return MPI_CHAR; } };
template <> struct MPI_TypeMapper<unsigned char> { static MPI_Datatype getType() { return MPI_UNSIGNED_CHAR; } };
template <> struct MPI_TypeMapper<short> { static MPI_Datatype getType() { return MPI_SHORT; } };
template <> struct MPI_TypeMapper<int> { static MPI_Datatype getType() { return MPI_INT; } };
template <> struct MPI_TypeMapper<long> { static MPI_Datatype getType() { return MPI_LONG; } };
template <> struct MPI_TypeMapper<long long> { static MPI_Datatype getType() { return MPI_LONG_LONG; } };
template <> struct MPI_TypeMapper<unsigned short>{ static MPI_Datatype getType() { return MPI_UNSIGNED_SHORT; } };
template <> struct MPI_TypeMapper<unsigned long> { static MPI_Datatype getType() { return MPI_UNSIGNED_LONG; } };
template <> struct MPI_TypeMapper<unsigned long long> { static MPI_Datatype getType() { return MPI_UNSIGNED_LONG_LONG; } };
template<typename TID = int>
struct MPI_t {
@ -28,8 +41,10 @@ struct MPI_t {
// Get the number of processes
int size_value, rank_value;
size_ = static_cast<ID_t>(MPI_Comm_size(MPI_COMM_WORLD, &size_value));
rank_ = static_cast<ID_t>(MPI_Comm_rank(MPI_COMM_WORLD, &rank_value));
MPI_Comm_size(MPI_COMM_WORLD, &size_value);
MPI_Comm_rank(MPI_COMM_WORLD, &rank_value);
size_ = static_cast<ID_t>(size_value);
rank_ = static_cast<ID_t>(rank_value);
// Get the name of the processor
char processor_name[MPI_MAX_PROCESSOR_NAME];
@ -43,18 +58,24 @@ struct MPI_t {
MPI_Finalize();
}
bool exchange(ID_t partner, const void *send_data, void *recv_data, int data_count, MPI_Datatype datatype) {
bool ret = true;
template<typename T>
void exchange(ID_t partner, const std::vector<T>& send_data, std::vector<T>& recv_data, int tag) {
using namespace std::string_literals;
MPI_Status status;
MPI_Sendrecv(
send_data, data_count, datatype, partner, 0,
recv_data, data_count, datatype, partner, 0,
MPI_Datatype datatype = MPI_TypeMapper<T>::getType();
int send_count = static_cast<int>(send_data.size());
int err = MPI_Sendrecv(
send_data.data(), send_count, datatype, partner, tag,
recv_data.data(), send_count, datatype, partner, tag,
MPI_COMM_WORLD, &status
);
if (status.MPI_ERROR != MPI_SUCCESS)
ret = false;
return ret;
if (err != MPI_SUCCESS) {
char err_msg[MPI_MAX_ERROR_STRING];
int msg_len;
MPI_Error_string(err, err_msg, &msg_len);
throw std::runtime_error("(MPI) MPI_Sendrecv() - " + std::string (err_msg) + '\n');
}
}
// Accessors
@ -71,12 +92,84 @@ private:
extern MPI_t<> mpi;
using mpi_id_t = MPI_t<>::ID_t;
template <typename Value_t>
struct ShadowedVec_t {
// STL requirements
using value_type = Value_t;
using iterator = typename std::vector<Value_t>::iterator;
using const_iterator = typename std::vector<Value_t>::const_iterator;
using size_type = typename std::vector<Value_t>::size_type;
// Dispatch to active vector
Value_t& operator[](size_type index) { return getActive()[index]; }
const Value_t& operator[](size_type index) const { return getActive()[index]; }
Value_t& at(size_type index) { return getActive().at(index); }
const Value_t& at(size_type index) const { return getActive().at(index); }
void push_back(const Value_t& value) { getActive().push_back(value); }
void push_back(Value_t&& value) { getActive().push_back(std::move(value)); }
void pop_back() { getActive().pop_back(); }
Value_t& front() { return getActive().front(); }
const Value_t& front() const { return getActive().front(); }
Value_t& back() { return getActive().back(); }
const Value_t& back() const { return getActive().back(); }
iterator begin() { return getActive().begin(); }
const_iterator begin() const { return getActive().begin(); }
iterator end() { return getActive().end(); }
const_iterator end() const { return getActive().end(); }
size_type size() const { return getActive().size(); }
void resize(size_t new_size) {
North.resize(new_size);
South.resize(new_size);
}
void reserve(size_t new_capacity) {
North.reserve(new_capacity);
South.reserve(new_capacity);
}
[[nodiscard]] size_t capacity() const { return getActive().capacity(); }
[[nodiscard]] bool empty() const { return getActive().empty(); }
void clear() { getActive().clear(); }
void swap(std::vector<Value_t>& other) { getActive().swap(other); }
// Switching vectors
void switch_active() { active = (active == north) ? south : north; }
// Accessors
const std::vector<Value_t>& getNorth() const { return North; }
const std::vector<Value_t>& getSouth() const { return South; }
std::vector<Value_t>& getActive() {
return (active == north) ? North : South;
}
const std::vector<Value_t>& getActive() const {
return (active == north) ? North : South;
}
std::vector<Value_t>& getShadow() {
return (active == north) ? South : North;
}
const std::vector<Value_t>& getShadow() const {
return (active == north) ? South : North;
}
private:
enum { north, south } active{north};
std::vector<Value_t> North{};
std::vector<Value_t> South{};
};
using distBuffer_t = ShadowedVec_t<distValue_t>;
extern distBuffer_t Data;
/*!
* A Logger for entire program.
*/
struct Log {
struct Endl {
} endl; //!< a tag object to to use it as a new line request.
struct Endl {} endl; //!< a tag object to to use it as a new line request.
//! We provide logging via << operator
template<typename T>

212
homework_2/src/distbitonic.cpp
View File

@ -1,212 +0,0 @@
/*!
* \file
* \brief Distributed bitonic implementation.
*
* \author
* Christos Choutouridis AEM:8997
* <cchoutou@ece.auth.gr>
*/
#include <vector>
#include <algorithm>
#include <cmath>
#if !defined DEBUG
#define NDEBUG
#endif
#include <cassert>
#include "distbitonic.hpp"
/*!
* Returns the ascending or descending configuration of the node's sequence based on
* the current node (MPI process) and the depth of the sorting network
*
* @param node The current node (MPI process)
* @return True if we need ascending configuration, false otherwise
*/
template <>
bool ascending<SortMode::Bubbletonic>(mpi_id_t node, [[maybe_unused]] size_t depth) noexcept {
return (node % 2) == 0;
}
/*!
* Returns the ascending or descending configuration of the node's sequence based on
* the current node (MPI process) and the depth of the sorting network
*
* @param node The current node (MPI process)
* @param depth The total depth of the sorting network (same for each step for a given network)
*
* @return True if we need ascending configuration, false otherwise
*/
template <>
bool ascending<SortMode::Bitonic>(mpi_id_t node, size_t depth) noexcept {
return !(node & (1 << depth));
}
/*!
* Returns the node's partner for data exchange during the sorting network iterations
* of Bubbletonic
*
* @param node The current node
* @param step The step of the sorting network
* @return The node id of the partner for data exchange
*/
template <>
mpi_id_t partner<SortMode::Bubbletonic>(mpi_id_t node, size_t step) noexcept {
// return (node % 2 == step % 2) ? node + 1 : node - 1;
return (((node+step) % 2) == 0) ? node + 1 : node - 1;
}
/*!
* Returns the node's partner for data exchange during the sorting network iterations
* of Bitonic
*
* @param node The current node
* @param step The step of the sorting network
* @return The node id of the partner for data exchange
*/
template <>
mpi_id_t partner<SortMode::Bitonic>(mpi_id_t node, size_t step) noexcept {
return (node ^ (1 << step));
}
/*!
* Predicate to check if a node keeps the small numbers during the bubbletonic sort network exchange.
*
* @param node The node for which we check
* @param partner The partner of the data exchange
* @return True if the node should keep the small values, false otherwise
*/
template <>
bool keepSmall<SortMode::Bubbletonic>(mpi_id_t node, mpi_id_t partner, [[maybe_unused]] size_t depth) noexcept {
assert(node != partner);
return (node < partner);
}
/*!
* Predicate to check if a node keeps the small numbers during the bitonic sort network exchange.
*
* @param node The node for which we check
* @param partner The partner of the data exchange
* @param depth The total depth of the sorting network (same for each step for a given network)
* @return True if the node should keep the small values, false otherwise
*/
template <>
bool keepSmall<SortMode::Bitonic>(mpi_id_t node, mpi_id_t partner, size_t depth) noexcept {
assert(node != partner);
return ascending<SortMode::Bitonic>(node, depth) == (node < partner);
}
/*!
* Predicate to check if the node is active in the current iteration of the bubbletonic
* sort exchange.
*
* @param node The node to check
* @param nodes The total number of nodes
* @return True if the node is active, false otherwise
*/
bool isActive(mpi_id_t node, mpi_id_t nodes) noexcept {
return (node >= 0) && (node < (nodes-1));
}
void exchange(mpi_id_t node, mpi_id_t partner) {
assert(node != partner);
}
void minmax(AllData_t& data, mpi_id_t node, mpi_id_t partner, bool keepsmall) {
for (size_t i = 0; i < data[node].size(); ++i) {
if (keepsmall && data[node][i] > data[partner][i])
std::swap(data[node][i], data[partner][i]);
if (!keepsmall && data[node][i] < data[partner][i])
std::swap(data[node][i], data[partner][i]);
}
}
void bubbletonic_network(AllData_t& data, mpi_id_t nodes, size_t depth) {
for (mpi_id_t node = 0 ; node < nodes ; ++node) { // Currently we do all nodes here!
auto part = partner<SortMode::Bubbletonic>(node, depth);
auto ks = keepSmall<SortMode::Bubbletonic>(node, part, 0);
if (isActive(node, nodes) && node < part) {
exchange(node, part);
minmax(data, node, part, ks);
// elbow-sort here
if (ascending<SortMode::Bubbletonic>(node, 0))
std::sort(data[node].begin(), data[node].end(), std::less<>());
else
std::sort(data[node].begin(), data[node].end(), std::greater<>());
if (ascending<SortMode::Bubbletonic>(part, 0))
std::sort(data[part].begin(), data[part].end(), std::less<>());
else
std::sort(data[part].begin(), data[part].end(), std::greater<>());
}
}
}
void distBubbletonic(mpi_id_t P, AllData_t& data) {
for (mpi_id_t node = 0 ; node < P ; ++node) { // Currently we do all nodes here!
// Initially sort to create the half part of a bitonic
if (ascending<SortMode::Bubbletonic>(node, 0))
std::sort(data[node].begin(), data[node].end(), std::less<>());
else
std::sort(data[node].begin(), data[node].end(), std::greater<>());
}
for (size_t depth = 0; depth < P-1; ++depth) {
bubbletonic_network(data, P, depth);
}
// Invert the descending ones
for (mpi_id_t node = 0 ; node < P ; ++node) { // Currently we do all nodes here!
if (!ascending<SortMode::Bubbletonic>(node, 0))
std::sort(data[node].begin(), data[node].end(), std::less<>());
}
}
void bitonic_network(AllData_t& data, mpi_id_t nodes, size_t depth) {
for (size_t step = depth; step > 0;) {
--step;
for (mpi_id_t node = 0; node < nodes; ++node) { // Currently we do all nodes here!
auto part = partner<SortMode::Bitonic>(node, step);
auto ks = keepSmall<SortMode::Bitonic>(node, part, depth);
if (node < part) {
exchange(node, part);
minmax(data, node, part, ks);
}
}
}
}
void distBitonic(mpi_id_t P, AllData_t& data) {
auto p = static_cast<uint32_t>(std::log2(P));
for (mpi_id_t node = 0 ; node < P ; ++node) { // Currently we do all nodes here!
// Initially sort to create the half part of a bitonic
if (ascending<SortMode::Bitonic>(node, 0))
std::sort(data[node].begin(), data[node].end(), std::less<>());
else
std::sort(data[node].begin(), data[node].end(), std::greater<>());
}
// Run through sort network using elbow-sort
for (size_t depth = 1; depth <= p; ++depth) {
bitonic_network(data, P, depth);
for (mpi_id_t node = 0 ; node < P ; ++node) { // Currently we do all nodes here!
// elbow-sort here
if (ascending<SortMode::Bitonic>(node, depth))
std::sort(data[node].begin(), data[node].end(), std::less<>());
else
std::sort(data[node].begin(), data[node].end(), std::greater<>());
}
}
}

31
homework_2/src/distsort.cpp Normal file
View File

@ -0,0 +1,31 @@
/*!
* \file
* \brief Distributed sort implementation.
*
* \author
* Christos Choutouridis AEM:8997
* <cchoutou@ece.auth.gr>
*/
#if !defined DEBUG
#define NDEBUG
#endif
#include <cassert>
#include "utils.hpp"
#include "distsort.hpp"
/*!
* Predicate to check if the node is active in the current iteration of the bubbletonic
* sort exchange.
*
* @param node The node to check
* @param nodes The total number of nodes
* @return True if the node is active, false otherwise
*/
bool isActive(mpi_id_t node, size_t nodes) noexcept {
assert(nodes > 0);
return (node >= 0) && (node < (nodes-1));
}

80
homework_2/src/main.cpp
View File

@ -9,15 +9,19 @@
#include <exception>
#include <iostream>
#include <algorithm>
#include "utils.hpp"
#include "config.h"
#include "distsort.hpp"
// Global session data
session_t session;
MPI_t<> mpi;
distBuffer_t Data;
Log logger;
Timing timer;
/*!
* A small command line argument parser
@ -30,29 +34,46 @@ bool get_options(int argc, char* argv[]){
for (int i=1 ; i<argc ; ++i) {
std::string arg(argv[i]); // get current argument
if (arg == "-x" || arg == "--xxxxx") {
if (i+2 < argc) {
// session.corpusMtxFile = std::string(argv[++i]);
// session.corpusDataSet = std::string(argv[++i]);
if (arg == "-q" || arg == "--array-size") {
if (i+1 < argc) {
session.arraySize = 1 << atoi(argv[++i]);
}
else
else {
status = false;
}
else if (arg == "-v" || arg == "--verbose")
}
else if (arg == "--ndebug") {
session.ndebug = true;
}
else if (arg == "-t" || arg == "--timing") {
session.timing = true;
}
else if (arg == "-v" || arg == "--verbose") {
session.verbose = true;
}
else if (arg == "-h" || arg == "--help") {
std::cout << "distBitonic - A distributed bitonic sort\n\n";
std::cout << "distBitonic -x <> [-v]\n";
std::cout << "distbitonic/distbubbletonic - A distributed bitonic sort\n\n";
std::cout << "distbitonic -q <> [--ndebug] [-v]\n";
std::cout << "distbitonic -h\n";
std::cout << "distbubbletonic -q <> [--ndebug] [-v]\n";
std::cout << "distbubbletonic -h\n";
std::cout << '\n';
std::cout << "Options:\n\n";
std::cout << " -q | --array-size <size>\n";
std::cout << " Selects the array size according to size = 2^q\n\n";
std::cout << " --ndebug\n";
std::cout << " Skip debug breakpoint when on debug build.\n\n";
std::cout << " -t | --timing\n";
std::cout << " Request timing measurements output to stdout.\n\n";
std::cout << " -v | --verbose\n";
std::cout << " Request a more verbose output to stdout.\n\n";
std::cout << " -h | --help\n";
std::cout << " Prints this and exit.\n\n";
std::cout << "Examples:\n\n";
std::cout << " ...Example case...:\n";
std::cout << " > distBitonic -x <xxxxx> \n\n";
std::cout << " mpirun -np 4 distbitonic -q 24\n";
std::cout << " Runs distbitonic in 4 MPI processes with 2^24 array points each\n\n";
std::cout << " mpirun -np 16 distbubbletonic -q 20\n";
std::cout << " Runs distbubbletonic in 16 MPI processes with 2^20 array points each\n\n";
exit(0);
}
@ -66,7 +87,6 @@ bool get_options(int argc, char* argv[]){
}
#if !defined TESTING
int main(int argc, char* argv[]) try {
// Initialize MPI environment
@ -76,7 +96,13 @@ int main(int argc, char* argv[]) try {
if (!get_options(argc, argv))
exit(1);
logger << "MPI environment initialized." <<
" Rank: " << mpi.rank() <<
" Size: " << mpi.size() <<
logger.endl;
#if defined DEBUG
#if defined TESTING
/*
* In case of a debug build we will wait here until sleep_wait
* will reset via debugger. In order to do that the user must attach
@ -86,24 +112,34 @@ int main(int argc, char* argv[]) try {
* $> gdb <program> <PID1>
* $> gdb <program> <PID2>
*/
#if defined TESTING
volatile bool sleep_wait = false;
#else
volatile bool sleep_wait = true;
#endif
while (sleep_wait)
while (sleep_wait && !session.ndebug)
sleep(1);
#endif
// Print off a hello world message
// std::cout << "Hello world from processor: " << mpi.processor_name
// << " rank " << mpi.world_rank
// << " out of " << mpi.world_size << " processors\n";
logger << "Initialize local array of " << session.arraySize << " elements" << logger.endl;
std::srand(unsigned(std::time(nullptr)));
Data.resize(session.arraySize);
std::generate(Data.begin(), Data.end(), std::rand);
if (mpi.rank() == 0)
logger << "Starting distributed sorting ... ";
timer.start();
#if CODE_VERSION == BUBBLETONIC
distBubbletonic(Data, mpi.size());
#else
distBitonic (Data, mpi.size());
#endif
timer.stop();
if (mpi.rank() == 0)
logger << " Done." << logger.endl;
std::string timeMsg = "rank " + std::to_string(mpi.rank());
timer.print_dt(timeMsg.c_str());
// distBitonic (2, Data);
// distBitonic (4, Data);
std::cout << "[Data]: Rank " << mpi.rank() << ": [" << (int)Data.front() << " .. " << (int)Data.back() << "]" << std::endl;
mpi.finalize();
return 0;
}

25
homework_2/test/tests_Bitonic.cpp
View File

@ -11,7 +11,7 @@
#include <algorithm> // rand/srand
#include <ctime> // rand/srand
#include "distbitonic.hpp"
#include "distsort.hpp"
@ -328,19 +328,20 @@ TEST(TdistBitonic_UT, keepsmall_test7) {
}
}
#if 0
TEST(TdistBitonic_UT, distBitonic_test1) {
AllData_t ts_Data {
Data_t (8), Data_t (8)
ShadowedVec_t (8), ShadowedVec_t (8)
};
std::srand(unsigned(std::time(nullptr)));
for (auto& v : ts_Data) {
(unsigned(std::time(nullptr)));
for (auto
std::srand& v : ts_Data) {
std::generate(v.begin(), v.end(), std::rand);
}
distBitonic(2, ts_Data);
auto max = std::numeric_limits<Data_t::value_type>::min();
auto max = std::numeric_limits<ShadowedVec_t::value_type>::min();
for (auto& v : ts_Data) {
EXPECT_EQ((max <= v[0]), true);
EXPECT_EQ(std::is_sorted(v.begin(), v.end()), true);
@ -350,7 +351,7 @@ TEST(TdistBitonic_UT, distBitonic_test1) {
TEST(TdistBitonic_UT, distBitonic_test2) {
AllData_t ts_Data {
Data_t (8), Data_t (8), Data_t (8), Data_t (8)
ShadowedVec_t (8), ShadowedVec_t (8), ShadowedVec_t (8), ShadowedVec_t (8)
};
std::srand(unsigned(std::time(nullptr)));
@ -360,7 +361,7 @@ TEST(TdistBitonic_UT, distBitonic_test2) {
distBitonic(4, ts_Data);
auto max = std::numeric_limits<Data_t::value_type>::min();
auto max = std::numeric_limits<ShadowedVec_t::value_type>::min();
for (auto& v : ts_Data) {
EXPECT_EQ((max <= v[0]), true);
EXPECT_EQ(std::is_sorted(v.begin(), v.end()), true);
@ -370,8 +371,8 @@ TEST(TdistBitonic_UT, distBitonic_test2) {
TEST(TdistBitonic_UT, distBitonic_test3) {
AllData_t ts_Data {
Data_t (32), Data_t (32), Data_t (32), Data_t (32),
Data_t (32), Data_t (32), Data_t (32), Data_t (32)
ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32),
ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32)
};
std::srand(unsigned(std::time(nullptr)));
@ -381,10 +382,12 @@ TEST(TdistBitonic_UT, distBitonic_test3) {
distBitonic(8, ts_Data);
auto max = std::numeric_limits<Data_t::value_type>::min();
auto max = std::numeric_limits<ShadowedVec_t::value_type>::min();
for (auto& v : ts_Data) {
EXPECT_EQ((max <= v[0]), true);
EXPECT_EQ(std::is_sorted(v.begin(), v.end()), true);
max = v.back();
}
}
#endif

19
homework_2/test/tests_Bubbletonic.cpp
View File

@ -11,7 +11,7 @@
#include <algorithm> // rand/srand
#include <ctime> // rand/srand
#include "distbitonic.hpp"
#include "distsort.hpp"
@ -120,10 +120,10 @@ TEST(TdistBubbletonic_UT, keepsmall_test2) {
}
#if 0
TEST(TdistBubbletonic_UT, distBubbletonic_test1) {
AllData_t ts_Data {
Data_t (8), Data_t (8)
ShadowedVec_t (8), ShadowedVec_t (8)
};
std::srand(unsigned(std::time(nullptr)));
@ -133,7 +133,7 @@ TEST(TdistBubbletonic_UT, distBubbletonic_test1) {
distBubbletonic(2, ts_Data);
auto max = std::numeric_limits<Data_t::value_type>::min();
auto max = std::numeric_limits<ShadowedVec_t::value_type>::min();
for (auto& v : ts_Data) {
EXPECT_EQ((max <= v[0]), true);
EXPECT_EQ(std::is_sorted(v.begin(), v.end()), true);
@ -144,7 +144,7 @@ TEST(TdistBubbletonic_UT, distBubbletonic_test1) {
TEST(TdistBubbletonic_UT, distBubbletonic_test2) {
AllData_t ts_Data {
Data_t (8), Data_t (8), Data_t (8), Data_t (8)
ShadowedVec_t (8), ShadowedVec_t (8), ShadowedVec_t (8), ShadowedVec_t (8)
};
std::srand(unsigned(std::time(nullptr)));
@ -154,7 +154,7 @@ TEST(TdistBubbletonic_UT, distBubbletonic_test2) {
distBubbletonic(4, ts_Data);
auto max = std::numeric_limits<Data_t::value_type>::min();
auto max = std::numeric_limits<ShadowedVec_t::value_type>::min();
for (auto& v : ts_Data) {
EXPECT_EQ((max <= v[0]), true);
EXPECT_EQ(std::is_sorted(v.begin(), v.end()), true);
@ -164,8 +164,8 @@ TEST(TdistBubbletonic_UT, distBubbletonic_test2) {
TEST(TdistBubbletonic_UT, distBubbletonic_test3) {
AllData_t ts_Data {
Data_t (32), Data_t (32), Data_t (32), Data_t (32),
Data_t (32), Data_t (32), Data_t (32), Data_t (32)
ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32),
ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32), ShadowedVec_t (32)
};
std::srand(unsigned(std::time(nullptr)));
@ -175,10 +175,11 @@ TEST(TdistBubbletonic_UT, distBubbletonic_test3) {
distBubbletonic(8, ts_Data);
auto max = std::numeric_limits<Data_t::value_type>::min();
auto max = std::numeric_limits<ShadowedVec_t::value_type>::min();
for (auto& v : ts_Data) {
EXPECT_EQ((max <= v[0]), true);
EXPECT_EQ(std::is_sorted(v.begin(), v.end()), true);
max = v.back();
}
}
#endif