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    Copyright 2005-2018 Intel Corporation.  All Rights Reserved.

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    No license under any patent, copyright, trade secret or other
    intellectual property right is granted to or conferred upon you by
    disclosure or delivery of the Materials, either expressly, by
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*/

/* Example program that computes Fibonacci numbers in different ways.
   Arguments are: [ Number [Threads [Repeats]]]
   The defaults are Number=500 Threads=1:4 Repeats=1.

   The point of this program is to check that the library is working properly.
   Most of the computations are deliberately silly and not expected to
   show any speedup on multiprocessors.
*/

// enable assertions
#ifdef NDEBUG
#undef NDEBUG
#endif

#include <cstdio>
#include <cstdlib>
#include <cassert>
#include <utility>
#include "tbb/task.h"
#include "tbb/task_scheduler_init.h"
#include "tbb/tick_count.h"
#include "tbb/blocked_range.h"
#include "tbb/concurrent_vector.h"
#include "tbb/concurrent_queue.h"
#include "tbb/concurrent_hash_map.h"
#include "tbb/parallel_while.h"
#include "tbb/parallel_for.h"
#include "tbb/parallel_reduce.h"
#include "tbb/parallel_scan.h"
#include "tbb/pipeline.h"
#include "tbb/atomic.h"
#include "tbb/mutex.h"
#include "tbb/spin_mutex.h"
#include "tbb/queuing_mutex.h"
#include "tbb/tbb_thread.h"

using namespace std;
using namespace tbb;

//! type used for Fibonacci number computations
typedef long long value;

//! Matrix 2x2 class
struct Matrix2x2
{
    //! Array of values
    value v[2][2];
    Matrix2x2() {}
    Matrix2x2(value v00, value v01, value v10, value v11) {
        v[0][0] = v00; v[0][1] = v01; v[1][0] = v10; v[1][1] = v11;
    }
    Matrix2x2 operator * (const Matrix2x2 &to) const; //< Multiply two Matrices
};
//! Identity matrix
static const Matrix2x2 MatrixIdentity(1, 0, 0, 1);
//! Default matrix to multiply
static const Matrix2x2 Matrix1110(1, 1, 1, 0);
//! Raw arrays matrices multiply
void Matrix2x2Multiply(const value a[2][2], const value b[2][2], value c[2][2]);

/////////////////////// Serial methods ////////////////////////

//! Plain serial sum
value SerialFib(int n)
{
    if(n < 2)
        return n;
    value a = 0, b = 1, sum; int i;
    for( i = 2; i <= n; i++ )
    {   // n is really index of Fibonacci number
        sum = a + b; a = b; b = sum;
    }
    return sum;
}
//! Serial n-1 matrices multiplication
value SerialMatrixFib(int n)
{
    value c[2][2], a[2][2] = {{1, 1}, {1, 0}}, b[2][2] = {{1, 1}, {1, 0}}; int i;
    for(i = 2; i < n; i++)
    {   // Using condition to prevent copying of values
        if(i & 1) Matrix2x2Multiply(a, c, b);
        else      Matrix2x2Multiply(a, b, c);
    }
    return (i & 1) ? c[0][0] : b[0][0]; // get result from upper left cell
}
//! Recursive summing. Just for complete list of serial algorithms, not used
value SerialRecursiveFib(int n)
{
    value result;
    if(n < 2)
        result = n;
    else
        result = SerialRecursiveFib(n - 1) + SerialRecursiveFib(n - 2);
    return result;
}
//! Introducing of queue method in serial
value SerialQueueFib(int n)
{
    concurrent_queue<Matrix2x2> Q;
    for(int i = 1; i < n; i++)
        Q.push(Matrix1110);
    Matrix2x2 A, B;
    while(true) {
        while( !Q.try_pop(A) ) this_tbb_thread::yield();
        if(Q.empty()) break;
        while( !Q.try_pop(B) ) this_tbb_thread::yield();
        Q.push(A * B);
    }
    return A.v[0][0];
}
//! Trying to use concurrent_vector
value SerialVectorFib(int n)
{
    concurrent_vector<value> A;
    A.grow_by(2);
    A[0] = 0; A[1] = 1;
    for( int i = 2; i <= n; i++)
    {
        A.grow_to_at_least(i+1);
        A[i] = A[i-1] + A[i-2];
    }
    return A[n];
}

///////////////////// Parallel methods ////////////////////////

// *** Serial shared by mutexes *** //

//! Shared glabals
value SharedA = 0, SharedB = 1; int SharedI = 1, SharedN;

//! Template task class which computes Fibonacci numbers with shared globals
template<typename M>
class SharedSerialFibBody {
    M &mutex;
public:
    SharedSerialFibBody( M &m ) : mutex( m ) {}
    //! main loop
    void operator()( const blocked_range<int>& range ) const {
        for(;;) {
            typename M::scoped_lock lock( mutex );
            if(SharedI >= SharedN) break;
            value sum = SharedA + SharedB;
            SharedA = SharedB; SharedB = sum;
            ++SharedI;
        }
    }
};

//! Root function
template<class M>
value SharedSerialFib(int n)
{
    SharedA = 0; SharedB = 1; SharedI = 1; SharedN = n; M mutex;
    parallel_for( blocked_range<int>(0,4,1), SharedSerialFibBody<M>( mutex ) );
    return SharedB;
}

// *** Serial shared by concurrent hash map *** //

//! Hash comparer
struct IntHashCompare {
    bool equal( const int j, const int k ) const { return j == k; }
    unsigned long hash( const int k ) const { return (unsigned long)k; }   
};
//! NumbersTable type based on concurrent_hash_map
typedef concurrent_hash_map<int, value, IntHashCompare> NumbersTable;
//! task for serial method using shared concurrent_hash_map
class ConcurrentHashSerialFibTask: public task {
    NumbersTable &Fib;
    int my_n;
public:
    //! constructor
    ConcurrentHashSerialFibTask( NumbersTable &cht, int n ) : Fib(cht), my_n(n) { }
    //! executing task
    task* execute() /*override*/ {
        for( int i = 2; i <= my_n; ++i ) { // there is no difference in to recycle or to make loop
            NumbersTable::const_accessor f1, f2; // same as iterators
            if( !Fib.find(f1, i-1) || !Fib.find(f2, i-2) ) {
                // Something is seriously wrong, because i-1 and i-2 must have been inserted 
                // earlier by this thread or another thread.
                assert(0);
            }
            value sum = f1->second + f2->second;
            NumbersTable::const_accessor fsum;
            Fib.insert(fsum, make_pair(i, sum)); // inserting
            assert( fsum->second == sum ); // check value
        }
        return 0;
    }
};

//! Root function
value ConcurrentHashSerialFib(int n)
{
    NumbersTable Fib; 
    bool okay;
    okay = Fib.insert( make_pair(0, 0) ); assert(okay); // assign initial values
    okay = Fib.insert( make_pair(1, 1) ); assert(okay);

    task_list list;
    // allocate tasks
    list.push_back(*new(task::allocate_root()) ConcurrentHashSerialFibTask(Fib, n));
    list.push_back(*new(task::allocate_root()) ConcurrentHashSerialFibTask(Fib, n));
    task::spawn_root_and_wait(list);
    NumbersTable::const_accessor fresult;
    okay = Fib.find( fresult, n );
    assert(okay);
    return fresult->second;
}

// *** Queue with parallel_for and parallel_while *** //

//! Stream of matrices
struct QueueStream {
    volatile bool producer_is_done;
    concurrent_queue<Matrix2x2> Queue;
    //! Get pair of matricies if present
    bool pop_if_present( pair<Matrix2x2, Matrix2x2> &mm ) {
        // get first matrix if present
        if(!Queue.try_pop(mm.first)) return false;
        // get second matrix if present
        if(!Queue.try_pop(mm.second)) {
            // if not, then push back first matrix
            Queue.push(mm.first); return false;
        }
        return true;
    }
};

//! Functor for parallel_for which fills the queue
struct parallel_forFibBody { 
    QueueStream &my_stream;
    //! fill functor arguments
    parallel_forFibBody(QueueStream &s) : my_stream(s) { }
    //! iterate thorough range
    void operator()( const blocked_range<int> &range ) const {
        int i_end = range.end();
        for( int i = range.begin(); i != i_end; ++i ) {
            my_stream.Queue.push( Matrix1110 ); // push initial matrix
        }
    }
};
//! Functor for parallel_while which process the queue
class parallel_whileFibBody
{
    QueueStream &my_stream;
    parallel_while<parallel_whileFibBody> &my_while;
public:
    typedef pair<Matrix2x2, Matrix2x2> argument_type;
    //! fill functor arguments
    parallel_whileFibBody(parallel_while<parallel_whileFibBody> &w, QueueStream &s)
        : my_while(w), my_stream(s) { }
    //! process pair of matrices
    void operator() (argument_type mm) const {
        mm.first = mm.first * mm.second;
        // note: it can run concurrently with QueueStream::pop_if_present()
        if(my_stream.Queue.try_pop(mm.second))
             my_while.add( mm ); // now, two matrices available. Add next iteration.
        else my_stream.Queue.push( mm.first ); // or push back calculated value if queue is empty
    }
};

//! Parallel queue's filling task
struct QueueInsertTask: public task {
    QueueStream &my_stream;
    int my_n;
    //! fill task arguments
    QueueInsertTask( int n, QueueStream &s ) : my_n(n), my_stream(s) { }
    //! executing task
    task* execute() /*override*/ {
        // Execute of parallel pushing of n-1 initial matrices
        parallel_for( blocked_range<int>( 1, my_n, 10 ), parallel_forFibBody(my_stream) ); 
        my_stream.producer_is_done = true; 
        return 0;
    }
};
//! Parallel queue's processing task
struct QueueProcessTask: public task {
    QueueStream &my_stream;
    //! fill task argument
    QueueProcessTask( QueueStream &s ) : my_stream(s) { }
    //! executing task
    task* execute() /*override*/ {
        while( !my_stream.producer_is_done || my_stream.Queue.unsafe_size()>1 ) {
            parallel_while<parallel_whileFibBody> w; // run while loop in parallel
            w.run( my_stream, parallel_whileFibBody( w, my_stream ) );
        }
        return 0;
    }
};
//! Root function
value ParallelQueueFib(int n)
{
    QueueStream stream;
    stream.producer_is_done = false;
    task_list list;
    list.push_back(*new(task::allocate_root()) QueueInsertTask( n, stream ));
    list.push_back(*new(task::allocate_root()) QueueProcessTask( stream ));
    // If there is only a single thread, the first task in the list runs to completion
    // before the second task in the list starts.
    task::spawn_root_and_wait(list);
    assert(stream.Queue.unsafe_size() == 1); // it is easy to lose some work
    Matrix2x2 M; 
    bool result = stream.Queue.try_pop( M ); // get last matrix
    assert( result );
    return M.v[0][0]; // and result number
}

// *** Queue with pipeline *** //

//! filter to fills queue
class InputFilter: public filter {
    tbb::atomic<int> N; //< index of Fibonacci number minus 1
public:
    concurrent_queue<Matrix2x2> Queue;
    //! fill filter arguments
    InputFilter( int n ) : filter(false /*is not serial*/) { N = n; }
    //! executing filter
    void* operator()(void*) /*override*/ {
        int n = --N;
        if(n <= 0) return 0;
        Queue.push( Matrix1110 );
        return &Queue;
    }
};
//! filter to process queue
class MultiplyFilter: public filter {
public:
    MultiplyFilter( ) : filter(false /*is not serial*/) { }
    //! executing filter
    void* operator()(void*p) /*override*/ {
        concurrent_queue<Matrix2x2> &Queue = *static_cast<concurrent_queue<Matrix2x2> *>(p);
        Matrix2x2 m1, m2;
        // get two elements
        while( !Queue.try_pop( m1 ) ) this_tbb_thread::yield(); 
        while( !Queue.try_pop( m2 ) ) this_tbb_thread::yield();
        m1 = m1 * m2; // process them
        Queue.push( m1 ); // and push back
        return this; // just nothing
    }
};
//! Root function
value ParallelPipeFib(int n)
{
    InputFilter input( n-1 );
    MultiplyFilter process;
    // Create the pipeline
    pipeline pipeline;
    // add filters
    pipeline.add_filter( input ); // first
    pipeline.add_filter( process ); // second

    input.Queue.push( Matrix1110 );
    // Run the pipeline
    pipeline.run( n ); // must be larger then max threads number
    pipeline.clear(); // do not forget clear the pipeline

    assert( input.Queue.unsafe_size()==1 );
    Matrix2x2 M; 
    bool result = input.Queue.try_pop( M ); // get last element
    assert( result );
    return M.v[0][0]; // get value
}

// *** parallel_reduce *** //

//! Functor for parallel_reduce
struct parallel_reduceFibBody {
    Matrix2x2 sum;
    int splitted;  //< flag to make one less operation for splitted bodies
    //! Constructor fills sum with initial matrix
    parallel_reduceFibBody() : sum( Matrix1110 ), splitted(0) { }
    //! Splitting constructor
    parallel_reduceFibBody( parallel_reduceFibBody& other, split ) : sum( Matrix1110 ), splitted(1/*note that it is splitted*/) {}
    //! Join point
    void join( parallel_reduceFibBody &s ) {
        sum = sum * s.sum;
    }
    //! Process multiplications
    void operator()( const blocked_range<int> &r ) {
        for( int k = r.begin() + splitted; k < r.end(); ++k )
            sum = sum * Matrix1110;
        splitted = 0; // reset flag, because this method can be reused for next range
    }
};
//! Root function
value parallel_reduceFib(int n)
{
    parallel_reduceFibBody b;
    parallel_reduce(blocked_range<int>(2, n, 3), b); // do parallel reduce on range [2, n) for b
    return b.sum.v[0][0];
}

// *** parallel_scan *** //

//! Functor for parallel_scan
struct parallel_scanFibBody {
    /** Though parallel_scan is usually used to accumulate running sums,
        it can be used to accumulate running products too. */
    Matrix2x2 product;
    /** Pointer to output sequence */
    value* const output;
    //! Constructor sets product to identity matrix
    parallel_scanFibBody(value* output_) : product( MatrixIdentity ), output(output_) {}
    //! Splitting constructor
    parallel_scanFibBody( parallel_scanFibBody &b, split) : product( MatrixIdentity ), output(b.output) {}
    //! Method for merging summary information from a, which was split off from *this, into *this.
    void reverse_join( parallel_scanFibBody &a ) {
        // When using non-commutative reduction operation, reverse_join
        // should put argument "a" on the left side of the operation.
        // The reversal from the argument order is why the method is
        // called "reverse_join" instead of "join".
        product = a.product * product;
    }
    //! Method for assigning final result back to original body.
    void assign( parallel_scanFibBody &b ) {
        product = b.product;
    }
    //! Compute matrix running product.
    /** Tag indicates whether is is the final scan over the range, or
        just a helper "prescan" that is computing a partial reduction. */
    template<typename Tag>
    void operator()( const blocked_range<int> &r, Tag tag) {
        for( int k = r.begin(); k < r.end(); ++k ) {
            // Code performs an "exclusive" scan, which outputs a value *before* updating the product.
            // For an "inclusive" scan, output the value after the update.
            if( tag.is_final_scan() )
                output[k] = product.v[0][1];
            product = product * Matrix1110;
        }
    }
};
//! Root function
value parallel_scanFib(int n)
{
    value* output = new value[n];
    parallel_scanFibBody b(output);
    parallel_scan(blocked_range<int>(0, n, 3), b);
    // output[0..n-1] now contains the Fibonacci sequence (modulo integer wrap-around).
    // Check the last two values for correctness.
    assert( n<2 || output[n-2]+output[n-1]==b.product.v[0][1] );
    delete[] output;
    return b.product.v[0][1];
}

// *** Raw tasks *** //

//! task class which computes Fibonacci numbers by Lucas formula
struct FibTask: public task {
    const int n;
    value& sum;
    value x, y;
    bool second_phase; //< flag of continuation
    // task arguments
    FibTask( int n_, value& sum_ ) : 
        n(n_), sum(sum_), second_phase(false)
    {}
    //! Execute task
    task* execute() /*override*/ {
        // Using Lucas' formula here
        if( second_phase ) { // children finished
            sum = n&1 ? x*x + y*y : x*x - y*y;
            return NULL;
        }
        if( n <= 2 ) {
            sum = n!=0;
            return NULL;
        } else {
            recycle_as_continuation();  // repeat this task when children finish
            second_phase = true; // mark second phase
            FibTask& a = *new( allocate_child() ) FibTask( n/2 + 1, x );
            FibTask& b = *new( allocate_child() ) FibTask( n/2 - 1 + (n&1), y );
            set_ref_count(2);
            spawn( a );
            return &b;
        }
    }
};
//! Root function
value ParallelTaskFib(int n) { 
    value sum;
    FibTask& a = *new(task::allocate_root()) FibTask(n, sum);
    task::spawn_root_and_wait(a);
    return sum;
}

/////////////////////////// Main ////////////////////////////////////////////////////

//! A closed range of int.
struct IntRange {
    int low;
    int high;
    void set_from_string( const char* s );
    IntRange( int low_, int high_ ) : low(low_), high(high_) {}
};

void IntRange::set_from_string( const char* s ) {
    char* end;
    high = low = strtol(s,&end,0);
    switch( *end ) {
    case ':': 
        high = strtol(end+1,0,0); 
        break;
    case '\0':
        break;
    default:
        printf("unexpected character = %c\n",*end);
    }
}

//! Tick count for start
static tick_count t0;

//! Verbose output flag
static bool Verbose = false;

typedef value (*MeasureFunc)(int);
//! Measure ticks count in loop [2..n]
value Measure(const char *name, MeasureFunc func, int n)
{
    value result;
    if(Verbose) printf("%s",name);
    t0 = tick_count::now();
    for(int number = 2; number <= n; number++)
        result = func(number);
    if(Verbose) printf("\t- in %f msec\n", (tick_count::now() - t0).seconds()*1000);
    return result;
}

//! program entry
int main(int argc, char* argv[])
{
    if(argc>1) Verbose = true;
    int NumbersCount = argc>1 ? strtol(argv[1],0,0) : 500;
    IntRange NThread(1,4);// Number of threads to use.
    if(argc>2) NThread.set_from_string(argv[2]);
    unsigned long ntrial = argc>3? (unsigned long)strtoul(argv[3],0,0) : 1;
    value result, sum;

    if(Verbose) printf("Fibonacci numbers example. Generating %d numbers..\n",  NumbersCount);

    result = Measure("Serial loop", SerialFib, NumbersCount);
    sum = Measure("Serial matrix", SerialMatrixFib, NumbersCount); assert(result == sum);
    sum = Measure("Serial vector", SerialVectorFib, NumbersCount); assert(result == sum);
    sum = Measure("Serial queue", SerialQueueFib, NumbersCount); assert(result == sum);
    // now in parallel
    for( unsigned long i=0; i<ntrial; ++i ) {
        for(int threads = NThread.low; threads <= NThread.high; threads *= 2)
        {
            task_scheduler_init scheduler_init(threads);
            if(Verbose) printf("\nThreads number is %d\n", threads);

            sum = Measure("Shared serial (mutex)\t", SharedSerialFib<mutex>, NumbersCount); assert(result == sum);
            sum = Measure("Shared serial (spin_mutex)", SharedSerialFib<spin_mutex>, NumbersCount); assert(result == sum);
            sum = Measure("Shared serial (queuing_mutex)", SharedSerialFib<queuing_mutex>, NumbersCount); assert(result == sum);
            sum = Measure("Shared serial (Conc.HashTable)", ConcurrentHashSerialFib, NumbersCount); assert(result == sum);
            sum = Measure("Parallel while+for/queue", ParallelQueueFib, NumbersCount); assert(result == sum);
            sum = Measure("Parallel pipe/queue\t", ParallelPipeFib, NumbersCount); assert(result == sum);
            sum = Measure("Parallel reduce\t\t", parallel_reduceFib, NumbersCount); assert(result == sum);
            sum = Measure("Parallel scan\t\t", parallel_scanFib, NumbersCount); assert(result == sum);
            sum = Measure("Parallel tasks\t\t", ParallelTaskFib, NumbersCount); assert(result == sum);
        }

    #ifdef __GNUC__
        if(Verbose) printf("Fibonacci number #%d modulo 2^64 is %lld\n\n", NumbersCount, result);
    #else
        if(Verbose) printf("Fibonacci number #%d modulo 2^64 is %I64d\n\n", NumbersCount, result);
    #endif
    }
    if(!Verbose) printf("TEST PASSED\n");
    return 0;
}

// Utils

void Matrix2x2Multiply(const value a[2][2], const value b[2][2], value c[2][2])
{
    for( int i = 0; i <= 1; i++)
        for( int j = 0; j <= 1; j++)
            c[i][j] = a[i][0]*b[0][j] + a[i][1]*b[1][j];
}

Matrix2x2 Matrix2x2::operator *(const Matrix2x2 &to) const
{
    Matrix2x2 result;
    Matrix2x2Multiply(v, to.v, result.v);
    return result;
}
