// Copyright (c) 2017-2023 Advanced Micro Devices, Inc. All rights reserved.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
// THE SOFTWARE.

#include <iostream>
#include <iomanip>
#include <vector>
#include <string>
#include <chrono>
#include <numeric>
#include <utility>
#include <type_traits>
#include <algorithm>

#include "cmdparser.hpp"

#include <cuda_runtime.h>
#include <curand.h>
#include <curand_kernel.h>
#include <curand_mtgp32_host.h>
#include <curand_mtgp32dc_p_11213.h>

#define CUPRAND_DEFAULT_MAX_BLOCK_SIZE 256

#define CUDA_CALL(x) do { \
    cudaError_t error = (x);\
    if(error!=cudaSuccess) { \
    printf("Error %d at %s:%d\n",error,__FILE__,__LINE__);\
    exit(EXIT_FAILURE);}} while(0)
#define CURAND_CALL(x) do { if((x)!=CURAND_STATUS_SUCCESS) { \
    printf("Error at %s:%d\n",__FILE__,__LINE__);\
    exit(EXIT_FAILURE);}} while(0)

#ifndef DEFAULT_RAND_N
const size_t DEFAULT_RAND_N = 1024 * 1024 * 128;
#endif

size_t next_power2(size_t x)
{
    size_t power = 1;
    while (power < x)
    {
        power *= 2;
    }
    return power;
}

template<typename GeneratorState>
__global__
__launch_bounds__(CUPRAND_DEFAULT_MAX_BLOCK_SIZE)
void init_kernel(GeneratorState * states,
                 const unsigned long long seed,
                 const unsigned long long offset)
{
    const unsigned int state_id = blockIdx.x * blockDim.x + threadIdx.x;
    GeneratorState state;
    curand_init(seed, state_id, offset, &state);
    states[state_id] = state;
}

template<typename GeneratorState, typename T, typename GenerateFunc, typename Extra>
__global__
__launch_bounds__(CUPRAND_DEFAULT_MAX_BLOCK_SIZE)
void generate_kernel(GeneratorState * states,
                     T * data,
                     const size_t size,
                     GenerateFunc generate_func,
                     const Extra extra)
{
    const unsigned int state_id = blockIdx.x * blockDim.x + threadIdx.x;
    const unsigned int stride = gridDim.x * blockDim.x;

    GeneratorState state = states[state_id];
    unsigned int index = state_id;
    while(index < size)
    {
        data[index] = generate_func(&state, extra);
        index += stride;
    }
    states[state_id] = state;
}

template<typename GeneratorState>
struct runner
{
    GeneratorState * states;

    runner(const size_t /* dimensions */,
           const size_t blocks,
           const size_t threads,
           const unsigned long long seed,
           const unsigned long long offset)
    {
        const size_t states_size = blocks * threads;
        CUDA_CALL(cudaMalloc(&states, states_size * sizeof(GeneratorState)));

        init_kernel<<<blocks, threads>>>(states, seed, offset);

        CUDA_CALL(cudaPeekAtLastError());
        CUDA_CALL(cudaDeviceSynchronize());
    }

    runner(const runner&)            = delete;
    runner(runner&&)                 = delete;
    runner& operator=(const runner&) = delete;
    runner& operator=(runner&&)      = delete;

    ~runner()
    {
        CUDA_CALL(cudaFree(states));
    }

    template<typename T, typename GenerateFunc, typename Extra>
    void generate(const size_t blocks,
                  const size_t threads,
                  T * data,
                  const size_t size,
                  const GenerateFunc& generate_func,
                  const Extra extra)
    {
        generate_kernel<<<blocks, threads>>>(states, data, size, generate_func, extra);
    }
};

template<typename T, typename GenerateFunc, typename Extra>
__global__
__launch_bounds__(CUPRAND_DEFAULT_MAX_BLOCK_SIZE)
void generate_kernel(curandStateMtgp32_t * states,
                     T * data,
                     const size_t size,
                     GenerateFunc generate_func,
                     const Extra extra)
{
    const unsigned int state_id = blockIdx.x;
    const unsigned int thread_id = threadIdx.x;
    unsigned int index = blockIdx.x * blockDim.x + threadIdx.x;
    unsigned int stride = gridDim.x * blockDim.x;

    __shared__ curandStateMtgp32_t state;

    if (thread_id == 0)
        state = states[state_id];
    __syncthreads();

    const size_t r = size%blockDim.x;
    const size_t size_rounded_up = r == 0 ? size : size + (blockDim.x - r);
    while(index < size_rounded_up)
    {
        auto value = generate_func(&state, extra);
        if(index < size)
            data[index] = value;
        index += stride;
    }
    __syncthreads();

    if (thread_id == 0)
        states[state_id] = state;
}

template<>
struct runner<curandStateMtgp32_t>
{
    curandStateMtgp32_t * states;
    mtgp32_kernel_params_t * d_param;

    runner(const size_t /* dimensions */,
           const size_t blocks,
           const size_t /* threads */,
           const unsigned long long seed,
           const unsigned long long /* offset */)
    {
        const size_t states_size = std::min((size_t)200, blocks);
        CUDA_CALL(cudaMalloc(&states, states_size * sizeof(curandStateMtgp32_t)));

        CUDA_CALL(cudaMalloc(&d_param, sizeof(mtgp32_kernel_params)));
        CURAND_CALL(curandMakeMTGP32Constants(mtgp32dc_params_fast_11213, d_param));
        CURAND_CALL(curandMakeMTGP32KernelState(states, mtgp32dc_params_fast_11213, d_param, states_size, seed));
    }

    runner(const runner&)            = delete;
    runner(runner&&)                 = delete;
    runner& operator=(const runner&) = delete;
    runner& operator=(runner&&)      = delete;

    ~runner()
    {
        CUDA_CALL(cudaFree(states));
        CUDA_CALL(cudaFree(d_param));
    }

    template<typename T, typename GenerateFunc, typename Extra>
    void generate(const size_t blocks,
                  const size_t /* threads */,
                  T * data,
                  const size_t size,
                  const GenerateFunc& generate_func,
                  const Extra extra)
    {
        generate_kernel<<<std::min((size_t)200, blocks), 256>>>(states, data, size, generate_func, extra);
    }
};

template<typename GeneratorState, typename SobolType>
__global__ __launch_bounds__(CUPRAND_DEFAULT_MAX_BLOCK_SIZE) void init_sobol_kernel(
    GeneratorState* states, SobolType* directions, SobolType offset)
{
    const unsigned int dimension = blockIdx.y;
    const unsigned int state_id = blockIdx.x * blockDim.x + threadIdx.x;
    GeneratorState     state;
    curand_init(&directions[dimension * sizeof(SobolType) * 8], offset + state_id, &state);
    states[gridDim.x * blockDim.x * dimension + state_id] = state;
}

template<typename GeneratorState, typename SobolType>
__global__ __launch_bounds__(CUPRAND_DEFAULT_MAX_BLOCK_SIZE) void init_scrambled_sobol_kernel(
    GeneratorState* states, SobolType* directions, SobolType* scramble_constants, SobolType offset)
{
    const unsigned int dimension = blockIdx.y;
    const unsigned int state_id  = blockIdx.x * blockDim.x + threadIdx.x;
    GeneratorState     state;
    curand_init(&directions[dimension * sizeof(SobolType) * 8],
                scramble_constants[dimension],
                offset + state_id,
                &state);
    states[gridDim.x * blockDim.x * dimension + state_id] = state;
}

// generate_kernel for the sobol generators
template<typename GeneratorState, typename T, typename GenerateFunc, typename Extra>
__global__ __launch_bounds__(CUPRAND_DEFAULT_MAX_BLOCK_SIZE) void generate_sobol_kernel(
    GeneratorState* states,
    T*              data,
    const size_t    size,
    GenerateFunc    generate_func,
    const Extra     extra)
{
    const unsigned int dimension = blockIdx.y;
    const unsigned int state_id = blockIdx.x * blockDim.x + threadIdx.x;
    const unsigned int stride = gridDim.x * blockDim.x;

    GeneratorState state  = states[gridDim.x * blockDim.x * dimension + state_id];
    const size_t   offset = dimension * size;
    unsigned int index = state_id;
    while(index < size)
    {
        data[offset + index] = generate_func(&state, extra);
        skipahead(stride - 1, &state);
        index += stride;
    }
    state = states[gridDim.x * blockDim.x * dimension + state_id];
    skipahead(size, &state);
    states[gridDim.x * blockDim.x * dimension + state_id] = state;
}

template<>
struct runner<curandStateSobol32_t>
{
    curandStateSobol32_t * states;
    size_t dimensions;

    runner(const size_t dimensions,
           const size_t blocks,
           const size_t threads,
           const unsigned long long /* seed */,
           const unsigned long long offset)
    {
        this->dimensions = dimensions;

        const size_t states_size = blocks * threads * dimensions;
        CUDA_CALL(cudaMalloc(&states, states_size * sizeof(curandStateSobol32_t)));

        curandDirectionVectors32_t * h_directions;
        CURAND_CALL(curandGetDirectionVectors32(&h_directions, CURAND_DIRECTION_VECTORS_32_JOEKUO6));
        unsigned int* directions;
        const size_t  size = dimensions * sizeof(unsigned int) * 32;
        CUDA_CALL(cudaMalloc(&directions, size));
        CUDA_CALL(cudaMemcpy(directions, h_directions, size, cudaMemcpyHostToDevice));

        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        init_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(
            states,
            directions,
            static_cast<unsigned int>(offset));

        CUDA_CALL(cudaPeekAtLastError());
        CUDA_CALL(cudaDeviceSynchronize());

        CUDA_CALL(cudaFree(directions));
    }

    runner(const runner&)            = delete;
    runner(runner&&)                 = delete;
    runner& operator=(const runner&) = delete;
    runner& operator=(runner&&)      = delete;

    ~runner()
    {
        CUDA_CALL(cudaFree(states));
    }

    template<typename T, typename GenerateFunc, typename Extra>
    void generate(const size_t blocks,
                  const size_t threads,
                  T * data,
                  const size_t size,
                  const GenerateFunc& generate_func,
                  const Extra extra)
    {
        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        generate_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(states,
                                                                       data,
                                                                       size / dimensions,
                                                                       generate_func,
                                                                       extra);
    }
};

template<>
struct runner<curandStateScrambledSobol32_t>
{
    curandStateScrambledSobol32_t* states;
    size_t                         dimensions;

    runner(const size_t dimensions,
           const size_t blocks,
           const size_t threads,
           const unsigned long long /* seed */,
           const unsigned long long offset)
    {
        this->dimensions = dimensions;

        const size_t states_size = blocks * threads * dimensions;
        CUDA_CALL(cudaMalloc(&states, states_size * sizeof(curandStateScrambledSobol32_t)));

        curandDirectionVectors32_t* h_directions;
        CURAND_CALL(
            curandGetDirectionVectors32(&h_directions, CURAND_DIRECTION_VECTORS_32_JOEKUO6));
        unsigned int* directions;
        const size_t  size = dimensions * sizeof(unsigned int) * 32;
        CUDA_CALL(cudaMalloc(&directions, size));
        CUDA_CALL(cudaMemcpy(directions, h_directions, size, cudaMemcpyHostToDevice));

        unsigned int* h_scramble_constants;
        CURAND_CALL(curandGetScrambleConstants32(&h_scramble_constants));
        unsigned int* scramble_constants;
        const size_t  constants_size = dimensions * sizeof(unsigned int);
        CUDA_CALL(cudaMalloc(&scramble_constants, constants_size));
        CUDA_CALL(cudaMemcpy(scramble_constants,
                             h_scramble_constants,
                             constants_size,
                             cudaMemcpyHostToDevice));

        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        init_scrambled_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(
            states,
            directions,
            scramble_constants,
            static_cast<unsigned int>(offset));

        CUDA_CALL(cudaPeekAtLastError());
        CUDA_CALL(cudaDeviceSynchronize());

        CUDA_CALL(cudaFree(directions));
        CUDA_CALL(cudaFree(scramble_constants));
    }

    runner(const runner&)            = delete;
    runner(runner&&)                 = delete;
    runner& operator=(const runner&) = delete;
    runner& operator=(runner&&)      = delete;

    ~runner()
    {
        CUDA_CALL(cudaFree(states));
    }

    template<typename T, typename GenerateFunc, typename Extra>
    void generate(const size_t        blocks,
                  const size_t        threads,
                  T*                  data,
                  const size_t        size,
                  const GenerateFunc& generate_func,
                  const Extra         extra)
    {
        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        generate_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(states,
                                                                       data,
                                                                       size / dimensions,
                                                                       generate_func,
                                                                       extra);
    }
};

template<>
struct runner<curandStateSobol64_t>
{
    curandStateSobol64_t * states;
    size_t dimensions;

    runner(const size_t dimensions,
           const size_t blocks,
           const size_t threads,
           const unsigned long long /* seed */,
           const unsigned long long offset)
    {
        this->dimensions = dimensions;

        const size_t states_size = blocks * threads * dimensions;
        CUDA_CALL(cudaMalloc(&states, states_size * sizeof(curandStateSobol64_t)));

        curandDirectionVectors64_t * h_directions;
        CURAND_CALL(curandGetDirectionVectors64(&h_directions, CURAND_DIRECTION_VECTORS_64_JOEKUO6));
        unsigned long long int* directions;
        const size_t            size = dimensions * sizeof(unsigned long long) * 64;
        CUDA_CALL(cudaMalloc(&directions, size));
        CUDA_CALL(cudaMemcpy(directions, h_directions, size, cudaMemcpyHostToDevice));

        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        init_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(states, directions, offset);

        CUDA_CALL(cudaPeekAtLastError());
        CUDA_CALL(cudaDeviceSynchronize());

        CUDA_CALL(cudaFree(directions));
    }

    runner(const runner&)            = delete;
    runner(runner&&)                 = delete;
    runner& operator=(const runner&) = delete;
    runner& operator=(runner&&)      = delete;

    ~runner()
    {
        CUDA_CALL(cudaFree(states));
    }

    template<typename T, typename GenerateFunc, typename Extra>
    void generate(const size_t        blocks,
                  const size_t        threads,
                  T*                  data,
                  const size_t        size,
                  const GenerateFunc& generate_func,
                  const Extra         extra)
    {
        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        generate_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(states,
                                                                       data,
                                                                       size / dimensions,
                                                                       generate_func,
                                                                       extra);
    }
};

template<>
struct runner<curandStateScrambledSobol64_t>
{
    curandStateScrambledSobol64_t* states;
    size_t                         dimensions;

    runner(const size_t dimensions,
           const size_t blocks,
           const size_t threads,
           const unsigned long long /* seed */,
           const unsigned long long offset)
    {
        this->dimensions = dimensions;

        const size_t states_size = blocks * threads * dimensions;
        CUDA_CALL(cudaMalloc(&states, states_size * sizeof(curandStateScrambledSobol64_t)));

        curandDirectionVectors64_t* h_directions;
        CURAND_CALL(
            curandGetDirectionVectors64(&h_directions, CURAND_DIRECTION_VECTORS_64_JOEKUO6));
        unsigned long long* directions;
        const size_t        size = dimensions * sizeof(unsigned long long) * 64;
        CUDA_CALL(cudaMalloc(&directions, size));
        CUDA_CALL(cudaMemcpy(directions, h_directions, size, cudaMemcpyHostToDevice));

        unsigned long long* h_scramble_constants;
        CURAND_CALL(curandGetScrambleConstants64(&h_scramble_constants));
        unsigned long long* scramble_constants;
        const size_t        constants_size = dimensions * sizeof(unsigned long long);
        CUDA_CALL(cudaMalloc(&scramble_constants, constants_size));
        CUDA_CALL(cudaMemcpy(scramble_constants,
                             h_scramble_constants,
                             constants_size,
                             cudaMemcpyHostToDevice));

        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        init_scrambled_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(states,
                                                                             directions,
                                                                             scramble_constants,
                                                                             offset);

        CUDA_CALL(cudaPeekAtLastError());
        CUDA_CALL(cudaDeviceSynchronize());

        CUDA_CALL(cudaFree(directions));
        CUDA_CALL(cudaFree(scramble_constants));
    }

    runner(const runner&)            = delete;
    runner(runner&&)                 = delete;
    runner& operator=(const runner&) = delete;
    runner& operator=(runner&&)      = delete;

    ~runner()
    {
        CUDA_CALL(cudaFree(states));
    }

    template<typename T, typename GenerateFunc, typename Extra>
    void generate(const size_t blocks,
                  const size_t threads,
                  T * data,
                  const size_t size,
                  const GenerateFunc& generate_func,
                  const Extra extra)
    {
        const size_t blocks_x = next_power2((blocks + dimensions - 1) / dimensions);
        generate_sobol_kernel<<<dim3(blocks_x, dimensions), threads>>>(states,
                                                                       data,
                                                                       size / dimensions,
                                                                       generate_func,
                                                                       extra);
    }
};

template<typename T, typename GeneratorState, typename GenerateFunc, typename Extra>
void run_benchmark(const cli::Parser& parser,
                   const GenerateFunc& generate_func,
                   const Extra extra)
{
    const size_t size = parser.get<size_t>("size");
    const size_t dimensions = parser.get<size_t>("dimensions");
    const size_t trials = parser.get<size_t>("trials");

    const size_t blocks = parser.get<size_t>("blocks");
    const size_t threads = parser.get<size_t>("threads");

    T * data;
    CUDA_CALL(cudaMalloc(&data, size * sizeof(T)));

    runner<GeneratorState> r(dimensions, blocks, threads, 12345ULL, 6789ULL);

    // Warm-up
    for (size_t i = 0; i < 5; i++)
    {
        r.generate(blocks, threads, data, size, generate_func, extra);
        CUDA_CALL(cudaPeekAtLastError());
        CUDA_CALL(cudaDeviceSynchronize());
    }
    CUDA_CALL(cudaDeviceSynchronize());

    // Measurement
    auto start = std::chrono::high_resolution_clock::now();
    for (size_t i = 0; i < trials; i++)
    {
        r.generate(blocks, threads, data, size, generate_func, extra);
    }
    CUDA_CALL(cudaPeekAtLastError());
    CUDA_CALL(cudaDeviceSynchronize());
    auto end = std::chrono::high_resolution_clock::now();
    std::chrono::duration<double, std::milli> elapsed = end - start;

    std::cout << std::fixed << std::setprecision(3)
              << "      "
              << "Throughput = "
              << std::setw(8) << (trials * size * sizeof(T)) /
                    (elapsed.count() / 1e3 * (1 << 30))
              << " GB/s, Samples = "
              << std::setw(8) << (trials * size) /
                    (elapsed.count() / 1e3 * (1 << 30))
              << " GSample/s, AvgTime (1 trial) = "
              << std::setw(8) << elapsed.count() / trials
              << " ms, Time (all) = "
              << std::setw(8) << elapsed.count()
              << " ms, Size = " << size
              << std::endl;

    CUDA_CALL(cudaFree(data));
}

template<typename GeneratorState>
void run_benchmarks(const cli::Parser& parser,
                    const std::string& distribution)
{
    if (distribution == "uniform-uint")
    {
        if (!std::is_same<GeneratorState, curandStateSobol64_t>::value &&
            !std::is_same<GeneratorState, curandStateScrambledSobol64_t>::value)
        {
            run_benchmark<unsigned int, GeneratorState>(parser,
                [] __device__ (GeneratorState * state, int) {
                    return curand(state);
                }, 0
            );
        }
    }
    if (distribution == "uniform-long-long")
    {
        if (std::is_same<GeneratorState, curandStateSobol64_t>::value ||
            std::is_same<GeneratorState, curandStateScrambledSobol64_t>::value)
        {
            run_benchmark<unsigned long long, GeneratorState>(parser,
                [] __device__ (GeneratorState * state, int) {
                    return curand(state);
                }, 0
            );
        }
    }
    if (distribution == "uniform-float")
    {
        run_benchmark<float, GeneratorState>(parser,
            [] __device__ (GeneratorState * state, int) {
                return curand_uniform(state);
            }, 0
        );
    }
    if (distribution == "uniform-double")
    {
        run_benchmark<double, GeneratorState>(parser,
            [] __device__ (GeneratorState * state, int) {
                return curand_uniform_double(state);
            }, 0
        );
    }
    if (distribution == "normal-float")
    {
        run_benchmark<float, GeneratorState>(parser,
            [] __device__ (GeneratorState * state, int) {
                return curand_normal(state);
            }, 0
        );
    }
    if (distribution == "normal-double")
    {
        run_benchmark<double, GeneratorState>(parser,
            [] __device__ (GeneratorState * state, int) {
                return curand_normal_double(state);
            }, 0
        );
    }
    if (distribution == "log-normal-float")
    {
        run_benchmark<float, GeneratorState>(parser,
            [] __device__ (GeneratorState * state, int) {
                return curand_log_normal(state, 0.0f, 1.0f);
            }, 0
        );
    }
    if (distribution == "log-normal-double")
    {
        run_benchmark<double, GeneratorState>(parser,
            [] __device__ (GeneratorState * state, int) {
                return curand_log_normal_double(state, 0.0, 1.0);
            }, 0
        );
    }
    if (distribution == "poisson")
    {
        const auto lambdas = parser.get<std::vector<double>>("lambda");
        for (double lambda : lambdas)
        {
            std::cout << "    " << "lambda "
                 << std::fixed << std::setprecision(1) << lambda << std::endl;
            run_benchmark<unsigned int, GeneratorState>(parser,
                [] __device__ (GeneratorState * state, double lambda) {
                    return curand_poisson(state, lambda);
                }, lambda
            );
        }
    }
    if (distribution == "discrete-poisson")
    {
        const auto lambdas = parser.get<std::vector<double>>("lambda");
        for (double lambda : lambdas)
        {
            std::cout << "    " << "lambda "
                 << std::fixed << std::setprecision(1) << lambda << std::endl;
            curandDiscreteDistribution_t discrete_distribution;
            CURAND_CALL(curandCreatePoissonDistribution(lambda, &discrete_distribution));
            run_benchmark<unsigned int, GeneratorState>(parser,
                [] __device__ (GeneratorState * state, curandDiscreteDistribution_t discrete_distribution) {
                    return curand_discrete(state, discrete_distribution);
                }, discrete_distribution
            );
            CURAND_CALL(curandDestroyDistribution(discrete_distribution));
        }
    }
}

const std::vector<std::string> all_engines = {"xorwow",
                                              "mrg32k3a",
                                              "mtgp32",
                                              // "mt19937",
                                              "philox",
                                              "sobol32",
                                              "scrambled_sobol32",
                                              "sobol64",
                                              "scrambled_sobol64"};

const std::vector<std::string> all_distributions = {
    "uniform-uint",
    // "uniform-long-long",
    "uniform-float",
    "uniform-double",
    "normal-float",
    "normal-double",
    "log-normal-float",
    "log-normal-double",
    "poisson",
    "discrete-poisson",
};

int main(int argc, char *argv[])
{
    cli::Parser parser(argc, argv);

    const std::string distribution_desc =
        "space-separated list of distributions:" +
        std::accumulate(all_distributions.begin(), all_distributions.end(), std::string(),
            [](std::string a, std::string b) {
                return a + "\n      " + b;
            }
        ) +
        "\n      or all";
    const std::string engine_desc =
        "space-separated list of random number engines:" +
        std::accumulate(all_engines.begin(), all_engines.end(), std::string(),
            [](std::string a, std::string b) {
                return a + "\n      " + b;
            }
        ) +
        "\n      or all";

    parser.set_optional<size_t>("size", "size", DEFAULT_RAND_N, "number of values");
    parser.set_optional<size_t>("dimensions", "dimensions", 1, "number of dimensions of quasi-random values");
    parser.set_optional<size_t>("trials", "trials", 20, "number of trials");
    parser.set_optional<size_t>("blocks", "blocks", 256, "number of blocks");
    parser.set_optional<size_t>("threads", "threads", 256, "number of threads in each block");
    parser.set_optional<std::vector<std::string>>("dis", "dis", {"uniform-uint"}, distribution_desc.c_str());
    parser.set_optional<std::vector<std::string>>("engine", "engine", {"philox"}, engine_desc.c_str());
    parser.set_optional<std::vector<double>>("lambda", "lambda", {10.0}, "space-separated list of lambdas of Poisson distribution");
    parser.run_and_exit_if_error();

    std::vector<std::string> engines;
    {
        auto es = parser.get<std::vector<std::string>>("engine");
        if (std::find(es.begin(), es.end(), "all") != es.end())
        {
            engines = all_engines;
        }
        else
        {
            for (auto e : all_engines)
            {
                if (std::find(es.begin(), es.end(), e) != es.end())
                    engines.push_back(e);
            }
        }
    }

    std::vector<std::string> distributions;
    {
        auto ds = parser.get<std::vector<std::string>>("dis");
        if (std::find(ds.begin(), ds.end(), "all") != ds.end())
        {
            distributions = all_distributions;
        }
        else
        {
            for (auto d : all_distributions)
            {
                if (std::find(ds.begin(), ds.end(), d) != ds.end())
                    distributions.push_back(d);
            }
        }
    }

    int version;
    CURAND_CALL(curandGetVersion(&version));
    int runtime_version;
    CUDA_CALL(cudaRuntimeGetVersion(&runtime_version));
    int device_id;
    CUDA_CALL(cudaGetDevice(&device_id));
    cudaDeviceProp props;
    CUDA_CALL(cudaGetDeviceProperties(&props, device_id));

    std::cout << "cuRAND: " << version << " ";
    std::cout << "Runtime: " << runtime_version << " ";
    std::cout << "Device: " << props.name;
    std::cout << std::endl << std::endl;

    for (auto engine : engines)
    {
        std::cout << engine << ":" << std::endl;
        for (auto distribution : distributions)
        {
            std::cout << "  " << distribution << ":" << std::endl;
            const std::string plot_name = engine + "-" + distribution;
            if (engine == "xorwow")
            {
                run_benchmarks<curandStateXORWOW_t>(parser, distribution);
            }
            else if (engine == "mrg32k3a")
            {
                run_benchmarks<curandStateMRG32k3a_t>(parser, distribution);
            }
            else if (engine == "philox")
            {
                run_benchmarks<curandStatePhilox4_32_10_t>(parser, distribution);
            }
            else if (engine == "sobol32")
            {
                run_benchmarks<curandStateSobol32_t>(parser, distribution);
            }
            else if(engine == "scrambled_sobol32")
            {
                run_benchmarks<curandStateScrambledSobol32_t>(parser, distribution);
            }
            else if (engine == "sobol64")
            {
                run_benchmarks<curandStateSobol64_t>(parser, distribution);
            }
            else if(engine == "scrambled_sobol64")
            {
                run_benchmarks<curandStateScrambledSobol64_t>(parser, distribution);
            }
            else if (engine == "mtgp32")
            {
                run_benchmarks<curandStateMtgp32_t>(parser, distribution);
            }
        }
        std::cout << std::endl;
    }

    return 0;
}