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Basic example of parallel execution (2 devices) and error in array size

I have modified the basic vector add buffers example from OneAPI to support the execution in two devices at the same time.

I have two problems:


1. I don't know how to achieve concurrent execution in both devices.  Can you provide what is the most efficient way to use OneAPI to achieve that? I need to execute multiple kernels in the same devices at the same time, getting the results as soon and sending again to the devices more chunks of work. Should I use host C++ threads to create each queue in an independent thread? I would appreciate some help and example, instead of trial and error per every idea.

2. When I increase the array size from 32 * 1024 * 10 to 32 * 1024 * 20 (in my system), the program segfaults (signal 11), with no errors. Why? Is not even close to the limits per device. What am I doing wrong using OneAPI?


I paste the example code:

 

//==============================================================
// Copyright © 2020 Intel Corporation
#include <CL/sycl.hpp>
#include <array>
#include <iostream>
#include <iomanip>
#include "dpc_common.hpp"

#define ARRAY_SIZE_SIGNAL11 (32 * 1024 * 20)
#define ARRAY_SIZE_WORKING (32 * 1024 * 10)
#define ARRAY_SIZE ARRAY_SIZE_WORKING
//#define ARRAY_SIZE ARRAY_SIZE_SIGNAL11

#define DEVS_ORDER_CPU_GPU 0
//#define DEVS_ORDER_CPU_GPU 1


using namespace cl::sycl;

enum class CheckpointName {
    InitQueue1,
    InitQueue2,
    Submitted1,
    Submitted2,
    Waited1,
    Waited2,
};

std::string checkpoint_name_to_str(CheckpointName name){
    switch(name){
        case CheckpointName::InitQueue1: {
            return "InitQueue1";
        } break;
        case CheckpointName::InitQueue2: {
            return "InitQueue2";
        } break;
        case CheckpointName::Submitted1: {
            return "Submitted1";
        } break;
        case CheckpointName::Submitted2: {
            return "Submitted2";
        } break;
        case CheckpointName::Waited1: {
            return "Waited1";
        } break;
        case CheckpointName::Waited2: {
            return "Waited2";
        } break;
    }
}

struct Checkpoint {
    CheckpointName name;
    std::chrono::time_point<std::chrono::high_resolution_clock> ts;
};

Checkpoint checkpoints[20];
int ncheckpoint = 0;

void ts_checkpoint(CheckpointName name){
    auto now = std::chrono::high_resolution_clock::now();
    Checkpoint ch{name, now};
    checkpoints[ncheckpoint++] = ch;
}

constexpr size_t array_size = ARRAY_SIZE;
typedef std::array<int, array_size> IntArray;

static void ReportTime(const std::string &msg, event e) {
    cl_ulong time_start =
            e.get_profiling_info<info::event_profiling::command_start>();

    cl_ulong time_end =
            e.get_profiling_info<info::event_profiling::command_end>();

    double elapsed = (time_end - time_start) / 1e6;
    std::cout << msg << elapsed << std::endl;
}

auto VectorAddOffset(queue &q, const IntArray &a_array, const IntArray &b_array,
                     IntArray &sum_parallel, size_t offset, size_t size, int dev) {
    size_t part_size = size;
    range<1> num_items{part_size};

    buffer a_buf(a_array);
    buffer b_buf(b_array);
    auto *ptr = sum_parallel.data();
    auto *ptr_offset = (ptr + (offset * sizeof(int)));
    buffer sum_buf(ptr, range<1>(sum_parallel.size())); // i cannot remove part of it

    event ev = q.submit([&](handler &h) {
        auto a = a_buf.get_access<access::mode::read>(h);
        auto b = b_buf.get_access<access::mode::read>(h);

        auto sum = sum_buf.get_access<access::mode::write>(h);

        h.parallel_for<class offset1>(range<1>(part_size), id<1>(0), [=](id<1> idx) {
            auto idx_offset = idx[0] + offset;
            int value = a[idx_offset] * b[idx_offset] / (b[idx_offset] + 1) + a[idx_offset] + b[idx_offset] / (b[idx_offset] + 2);
            sum[idx_offset] = value + a[idx_offset] + b[idx_offset] / (b[idx_offset] + 2);
        });
    });

    if (dev == 1) {
        ts_checkpoint(CheckpointName::Submitted1);
    } else {
        ts_checkpoint(CheckpointName::Submitted2);
    }

    return ev;
}

void InitializeArray(IntArray &a) {
    for (size_t i = 0; i < a.size(); i++) a[i] = i;
}

void InitializeArray(IntArray &a, int value) {
    for (size_t i = 0; i < a.size(); i++) a[i] = value;
}

int main() {
    // Create device selector for the device of your interest.
#if DEVS_ORDER_CPU_GPU == 1
    cpu_selector d_selector;
    gpu_selector d2_selector;
#else
    gpu_selector d_selector;
    cpu_selector d2_selector;
#endif


    IntArray a, b, sum_sequential, sum_parallel;

    InitializeArray(a);
    InitializeArray(b);
    InitializeArray(sum_parallel, -1);

    auto devs_str = getenv("DEVS");
    auto devs = 2;
    if (devs_str != NULL) {
        devs = atoi(devs_str);
    }
    event e1, e2;

    auto start = std::chrono::high_resolution_clock::now();
    std::chrono::time_point<std::chrono::high_resolution_clock> end1, end2, end3, end4;
    auto prop_list = property_list{property::queue::enable_profiling()};
    if (devs == 1) {
        try {
            queue q(d_selector, dpc::exception_handler, prop_list);

            std::cout << "Running on device1: "
                      << q.get_device().get_info<info::device::name>() << "\n";
            std::cout << "Vector size: " << a.size() << "\n";

            e1 = VectorAddOffset(q, a, b, sum_parallel, 0, array_size, 1);
            end1 = std::chrono::high_resolution_clock::now();

            q.wait_and_throw();
            ts_checkpoint(CheckpointName::Waited1);
        } catch (exception const &e) {
            std::cout << "An exception is caught for vector add.\n";
            std::terminate();
        }
    } else if (devs == 2) {
        try {
            queue q(d2_selector, dpc::exception_handler, prop_list);

            std::cout << "Running on device2: "
                      << q.get_device().get_info<info::device::name>() << "\n";
            std::cout << "Vector size: " << a.size() << "\n";

            e1 = VectorAddOffset(q, a, b, sum_parallel, 0, array_size, 1);
            end1 = std::chrono::high_resolution_clock::now();

            q.wait_and_throw();
            ts_checkpoint(CheckpointName::Waited1);
        } catch (exception const &e) {
            std::cout << "An exception is caught for vector add.\n";
            std::terminate();
        }
    } else {
        try {
            queue q(d_selector, dpc::exception_handler, prop_list);
            queue q2(d2_selector, dpc::exception_handler, prop_list);

            std::cout << "Running on device1: "
                      << q.get_device().get_info<info::device::name>() << "\n";
            std::cout << "Running on device2: "
                      << q2.get_device().get_info<info::device::name>() << "\n";
            std::cout << "Vector size: " << a.size() << "\n";

            e1 = VectorAddOffset(q, a, b, sum_parallel, 0, array_size / 2, 1);
            end1 = std::chrono::high_resolution_clock::now();
            e2 = VectorAddOffset(q2, a, b, sum_parallel, array_size / 2, array_size / 2, 2);
            end2 = std::chrono::high_resolution_clock::now();

            q.wait_and_throw();
            ts_checkpoint(CheckpointName::Waited1);
            q2.wait_and_throw();
            ts_checkpoint(CheckpointName::Waited2);
        } catch (exception const &e) {
            std::cout << "An exception is caught for vector add.\n";
            std::terminate();
        }
    }
    end3 = std::chrono::high_resolution_clock::now();

    for (size_t i = 0; i < sum_sequential.size(); i++){
        int value = a[i] * b[i] / (b[i] + 1) + a[i] + b[i] / (b[i] + 2);
        sum_sequential[i] = value + a[i] + b[i] / (b[i] + 2);
    }

    for (size_t i = 0; i < sum_sequential.size(); i++) {
        if (sum_parallel[i] != sum_sequential[i]) {
            std::cout << "Vector add failed on device.\n";
        }
    }

    int indices[]{0, 1, 2, (a.size() - 1)};
    constexpr size_t indices_size = sizeof(indices) / sizeof(int);

    for (int i = 0; i < indices_size; i++) {
        int j = indices[i];
        if (i == indices_size - 1) std::cout << "...\n";
        std::cout << "[" << j << "]: " << a[j] << " + " << b[j] << " = "
                  << sum_parallel[j] << "\n";
    }

    std::cout << "Vector add successfully completed on device.\n";
    end4 = std::chrono::high_resolution_clock::now();
    std::chrono::duration<double> diff1 = end1 - start;
    std::chrono::duration<double> diff2 = end2 - start;
    std::chrono::duration<double> diff3 = end3 - start;
    std::chrono::duration<double> diff4 = end4 - start;

    std::cout << "diff vector add1: " << std::fixed << std::setprecision(5) << diff1.count() << "\n";
    if (devs == 3) {
        std::cout << "diff vector add2: " << std::fixed << std::setprecision(5) << diff2.count() << "\n";
    }
    std::cout << "diff trycatch end: " << std::fixed << std::setprecision(5) << diff3.count() << "\n";
    std::cout << "diff end: " << std::fixed << std::setprecision(5) << diff3.count() << "\n";

    ReportTime("e1: ", e1);
    if (devs == 3) {
        ReportTime("e2: ", e2);
    }

    for (int i=0; i<ncheckpoint; ++i){
        Checkpoint cp = checkpoints[i];
        std::chrono::duration<double> diff = cp.ts - start;
        std::cout << i << " " << checkpoint_name_to_str(cp.name) << ": " << std::fixed << std::setprecision(5) << (diff.count()) << "\n";
    }

    return 0;
}

 


And how it should be executed:

 

$ DEVS=1 /usr/bin/time ./vector-add
Running on device1: Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector size: 327680
[0]: 0 + 0 = 0
[1]: 1 + 1 = 2
[2]: 2 + 2 = 5
...
[327679]: 327679 + 327679 = 655357
Vector add successfully completed on device.
diff vector add1: 1.00805
diff trycatch end: 1.00807
diff end: 1.00807
e1: 0.70930
0 Submitted1: 1.00693
1 Waited1: 1.00806
1.09user 0.12system 0:01.24elapsed 97%CPU (0avgtext+0avgdata 186668maxresident)k
0inputs+0outputs (0major+19561minor)pagefaults 0swaps

$ DEVS=2 /usr/bin/time ./vector-add
Running on device2: Intel(R) Gen9
Vector size: 327680
[0]: 0 + 0 = 0
[1]: 1 + 1 = 2
[2]: 2 + 2 = 5
...
[327679]: 327679 + 327679 = 655357
Vector add successfully completed on device.
diff vector add1: 0.94280
diff trycatch end: 0.94289
diff end: 0.94289
e1: 0.60930
0 Submitted1: 0.94129
1 Waited1: 0.94281
0.84user 0.16system 0:01.04elapsed 96%CPU (0avgtext+0avgdata 161052maxresident)k
0inputs+0outputs (0major+25701minor)pagefaults 0swaps

$ DEVS=3 /usr/bin/time ./vector-add
Running on device1: Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Running on device2: Intel(R) Gen9
Vector size: 327680
[0]: 0 + 0 = 0
[1]: 1 + 1 = 2
[2]: 2 + 2 = 5
...
[327679]: 327679 + 327679 = 655357
Vector add successfully completed on device.
diff vector add1: 0.82789
diff vector add2: 1.27030
diff trycatch end: 1.27035
diff end: 1.27035
e1: 0.39423
e2: 0.24327
0 Submitted1: 0.82691
1 Submitted2: 1.26955
2 Waited1: 1.27031
3 Waited2: 1.27032
1.26user 0.18system 0:01.47elapsed 97%CPU (0avgtext+0avgdata 261140maxresident)k
0inputs+0outputs (0major+36073minor)pagefaults 0swaps


 

 

With `DEVS=1` we select the CPU, with `DEVS=2` the iGPU and with `DEVS=3` both devices. As we can see, the kernel time execution (report time) is less because the workload is splitted, but the total times in submitting and waiting are accumulative, not splitted (not parallel). So, the execution is not parallel between both devices.

If I swap the queues execution (VectorAddOffset, first gpu then cpu with DEVS_ORDER_CPU_GPU to 0):

 

$ DEVS=3 /usr/bin/time ./vector-add
Running on device1: Intel(R) Gen9
Running on device2: Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector size: 327680
[0]: 0 + 0 = 0
[1]: 1 + 1 = 2
[2]: 2 + 2 = 5
...
[327679]: 327679 + 327679 = 655357
Vector add successfully completed on device.
diff vector add1: 1.18999
diff vector add2: 1.58355
diff trycatch end: 1.58394
diff end: 1.58394
e1: 0.23140
e2: 0.82824
0 Submitted1: 1.18879
1 Submitted2: 1.58198
2 Waited1: 1.58358
3 Waited2: 1.58359
1.57user 0.17system 0:01.77elapsed 98%CPU (0avgtext+0avgdata 252960maxresident)k
0inputs+0outputs (0major+34734minor)pagefaults 0swaps

 

Not really different results from the previous order.

It is really important to know the "official" way to do this by OneAPI, what is the most performant way. Since I will compare with pure OpenCL. How to do parallel offload appropriately?

I thought about using C++ threads and each thread with an independent queue, but better to know directly from Intel.



Finally, just change the preprocessor var to ARRAY_SIZE_SIGNAL11 to see the segfault (using a intel cpu i5 +gpu intel graphics).

 

$ DEVS=1 /usr/bin/time ./vector-add
Command terminated by signal 11
0.01user 0.00system 0:00.96elapsed 1%CPU (0avgtext+0avgdata 7560maxresident)k
0inputs+0outputs (0major+491minor)pagefaults 0swaps

 

 

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Hi,

 

Possible explanation for your questions:

 

  1. For the kernels to run concurrently on multiple devices (or even on the same device for that matter), there must be no data dependency amongst them. Accessors are responsible for creating Directed Acyclic Graph(DAG) to represent data dependencies across various kernels(within the queue as well as across queues). If there is no data dependency found, the execution will take place concurrently across multiple devices(or within the same device as well). In your case, concurrent kernel execution is limited by host side(since the host execution is serial). I'd suggest you to modify the code in this way, in order to achieve concurrency.                              
    #include <CL/sycl.hpp>
    #include <iostream>
    
    int main() {
    const int N = 1024;
    auto R = sycl::range<1>(N/2); //Half Range for split
    std::vector<int> v1(N); //Input Vector 1
    std::vector<int> v2(N); //Input Vector 2
    for(int i=0;i<N;i++) {
        v1[i] = i;
        v2[i] = N-i;
    }
    sycl::queue cpuQ(sycl::cpu_selector{}); //CPU Queue
    sycl::queue gpuQ(sycl::gpu_selector{}); //GPU Queue
    
    {
    //Splitting input data into 2 halves for CPU and iGPU
    sycl::buffer<int,1> bufgpuv1(v1.data(), R); 
    sycl::buffer<int,1> bufgpuv2(v2.data(), R);
    sycl::buffer<int,1> bufcpuv1((v1.data()+(N/2)), R);
    sycl::buffer<int,1> bufcpuv2((v2.data()+(N/2)), R);
    
    std::cout<<"Running on: "<<gpuQ.get_device().get_info<sycl::info::device::name>()<<" and "
    <<cpuQ.get_device().get_info<sycl::info::device::name>()<<"\n";
    gpuQ.submit([&](sycl::handler &h){
        auto agpuv1 = bufgpuv1.get_access<sycl::access::mode::read_write>(h);
        auto agpuv2 = bufgpuv2.get_access<sycl::access::mode::read_write>(h);
        h.parallel_for (R, [=](sycl::id<1> i){
            agpuv1[i]+=agpuv2[i]; //Stpring output in Input 1
        });
    });
    cpuQ.submit([&](sycl::handler &h){
        auto acpuv1 = bufcpuv1.get_access<sycl::access::mode::read_write>(h);
        auto acpuv2 = bufcpuv2.get_access<sycl::access::mode::read_write>(h);
        h.parallel_for(R, [=](sycl::id<1> i){
            acpuv1[i]+=acpuv2[i]; //Storing output in Input 1
        });
    });
    }
    /*std::cout<<"Sample values on GPU and CPU\n";
    for(int i =0; i < 10; i++)
        std::cout<<v1[i]<<" "<<v1[N/2+i]<<"\n";*/
    std::vector<int> validate_vector(N,N); //Validating
    validate_vector==v1?std::cout<<"Vector addition: Success\n":std::cout<<"Vector addition: Failure\n";
    
    return 0;
    }
  2. Every device comes with its own maximum allocatable memory, which is fixed by OpenCL/SYCL standards. Essentially, it is the maximum memory that can be allocated on a single data structure(array in your case). To know about the limits, run "clinfo" command on the terminal. For instance on Intel iGPU(Gen 9), you should see something like:

 

Max memory allocation              4294959104 (4GiB)  

 

           In your case, its probably exceeding this limit(4 GB)

 

Let me know if you face any issues with the code.

 

Regards,

Rahul

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New Contributor I
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Hi, Rahul.

Thanks for the code example, really. I wrote the answer but it was suddenly authenticated and I lost everything... I was writing for 2h, carefully, so I will summarize:

1.1. I modified the example to study the performance.

 

#include <CL/sycl.hpp>
#include <iostream>

void cpu(int N, bool check) {
  std::cout << "CPU N: " << N << " size: " << sizeof(N) * N << " B\n";

  auto R = sycl::range<1>(N);
  std::vector<int> v1(N); //Input Vector 1
  std::vector<int> v2(N); //Input Vector 2
  for (int i = 0; i < N; i++) {
    v1[i] = i;
    v2[i] = N - i;
  }
  sycl::queue cpuQ(sycl::cpu_selector{});

  {
    sycl::buffer<int, 1> bufcpuv1(v1.data(), R);
    sycl::buffer<int, 1> bufcpuv2(v2.data(), R);

    std::cout << "Running on: " << cpuQ.get_device().get_info<sycl::info::device::name>() << "\n";
    cpuQ.submit([&](sycl::handler &h) {
      auto acpuv1 = bufcpuv1.get_access<sycl::access::mode::read_write>(h);
      auto acpuv2 = bufcpuv2.get_access<sycl::access::mode::read_write>(h);
      h.parallel_for(R, [=](sycl::id<1> i) {
        acpuv1[i] += acpuv2[i]; //Storing output in Input 1
      });
    });
  }
/*std::cout<<"Sample values on GPU and CPU\n";
for(int i =0; i < 10; i++)
    std::cout<<v1[i]<<" "<<v1[N/2+i]<<"\n";*/
  if (check) {
    std::vector<int> validate_vector(N, N); //Validating
    validate_vector == v1 ? std::cout << "Vector addition: Success\n" : std::cout << "Vector addition: Failure\n";
  }
}

void gpu(int N, bool check) {
  std::cout << "GPU N: " << N << " size: " << sizeof(N) * N << " B\n";

  auto R = sycl::range<1>(N);
  std::vector<int> v1(N); //Input Vector 1
  std::vector<int> v2(N); //Input Vector 2
  for (int i = 0; i < N; i++) {
    v1[i] = i;
    v2[i] = N - i;
  }
  sycl::queue gpuQ(sycl::gpu_selector{});

  {
    sycl::buffer<int, 1> bufgpuv1(v1.data(), R);
    sycl::buffer<int, 1> bufgpuv2(v2.data(), R);

    std::cout << "Running on: " << gpuQ.get_device().get_info<sycl::info::device::name>() << "\n";
    gpuQ.submit([&](sycl::handler &h) {
      auto agpuv1 = bufgpuv1.get_access<sycl::access::mode::read_write>(h);
      auto agpuv2 = bufgpuv2.get_access<sycl::access::mode::read_write>(h);
      h.parallel_for(R, [=](sycl::id<1> i) {
        agpuv1[i] += agpuv2[i]; //Storing output in Input 1
      });
    });
  }
/*std::cout<<"Sample values on GPU and CPU\n";
for(int i =0; i < 10; i++)
    std::cout<<v1[i]<<" "<<v1[N/2+i]<<"\n";*/
  if (check) {

    std::vector<int> validate_vector(N, N); //Validating
    validate_vector == v1 ? std::cout << "Vector addition: Success\n" : std::cout << "Vector addition: Failure\n";
  }
}

void cpugpu(int N, float prop_cpu, bool check) {
  std::cout << "CPUGPU N: " << N << " size: " << sizeof(N) * N << " B\n";
  int part_cpu = N * prop_cpu; // 0.0 > prop_cpu < 1.0
  int part_gpu = N - part_cpu;
  std::cout << "   CPU with N: " << part_cpu << " prop: " << 100 * (prop_cpu) << " %\n";
  std::cout << "   GPU with N: " << part_gpu << " prop: " << 100 * (1 - prop_cpu) << " %\n";

  auto Rcpu = sycl::range<1>(part_cpu);
  auto Rgpu = sycl::range<1>(part_gpu);
  std::vector<int> v1(N); //Input Vector 1
  std::vector<int> v2(N); //Input Vector 2
  for (int i = 0; i < N; i++) {
    v1[i] = i;
    v2[i] = N - i;
  }
  sycl::queue cpuQ(sycl::cpu_selector{}); //CPU Queue
  sycl::queue gpuQ(sycl::gpu_selector{}); //GPU Queue

  {
    sycl::buffer<int, 1> bufgpuv1(v1.data(), Rgpu);
    sycl::buffer<int, 1> bufgpuv2(v2.data(), Rgpu);
    sycl::buffer<int, 1> bufcpuv1((v1.data() + (part_gpu)), Rcpu);
    sycl::buffer<int, 1> bufcpuv2((v2.data() + (part_gpu)), Rcpu);

    std::cout << "Running on: " << gpuQ.get_device().get_info<sycl::info::device::name>() << " and "
              << cpuQ.get_device().get_info<sycl::info::device::name>() << "\n";
    gpuQ.submit([&](sycl::handler &h) {
      auto agpuv1 = bufgpuv1.get_access<sycl::access::mode::read_write>(h);
      auto agpuv2 = bufgpuv2.get_access<sycl::access::mode::read_write>(h);
      h.parallel_for(Rgpu, [=](sycl::id<1> i) {
        agpuv1[i] += agpuv2[i]; //Stpring output in Input 1
      });
    });
    cpuQ.submit([&](sycl::handler &h) {
      auto acpuv1 = bufcpuv1.get_access<sycl::access::mode::read_write>(h);
      auto acpuv2 = bufcpuv2.get_access<sycl::access::mode::read_write>(h);
      h.parallel_for(Rcpu, [=](sycl::id<1> i) {
        acpuv1[i] += acpuv2[i]; //Storing output in Input 1
      });
    });
  }
/*std::cout<<"Sample values on GPU and CPU\n";
for(int i =0; i < 10; i++)
    std::cout<<v1[i]<<" "<<v1[N/2+i]<<"\n";*/
  if (check) {
    std::vector<int> validate_vector(N, N); //Validating
    validate_vector == v1 ? std::cout << "Vector addition: Success\n" : std::cout << "Vector addition: Failure\n";
  }
}

int main(int argc, char *argv[]) {
  std::string type;
  std::string n;
  bool check = false;
  float prop = 0.5;
  int N = 0;
  if (argc > 1) {
    type = argv[1];
  }
  if (argc > 2) {
    N = std::stoi(argv[2]);
  }
  if (argc > 3) {
    prop = std::stof(argv[3]);
    if (prop <= 0.0 || prop >= 1.0){
      std::cout << "prop cpu should be between 0.0 and 1.0 (both exclusive)\n";
      return 1;
    }
  }
  if (argc > 4) {
    check = std::string(argv[4]) == "check";
  }
  bool valid = false;
  if (N > 0) {
    if (type == "cpugpu") {
      cpugpu(N, prop, check);
      valid = true;
    } else if (type == "cpu") {
      cpu(N, check);
      valid = true;
    } else if (type == "gpu") {
      gpu(N, check);
      valid = true;
    }
  }

  if (!valid) {
    std::cout << "usage: (cpu|gpu|cpugpu) <N> [<prop float> (check)]\n";
    std::cout << "   prop cpu: 0.5 (default)\n";
    return 1;
  }

  return 0;
}

 

I run some experiments, first, comparing cpu with gpu and cpugpu:

 

$ for dev in cpu gpu cpugpu; do /usr/bin/time ./vector-add $dev $(( 1024 * 1024 * 200 )); done

CPU N: 209715200 size: 838860800 B
Running on: Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
3.92user 0.60system 0:03.99elapsed 113%CPU (0avgtext+0avgdata 1815676maxresident)k
0inputs+0outputs (0major+426952minor)pagefaults 0swaps
GPU N: 209715200 size: 838860800 B
Running on: Intel(R) Gen9
3.53user 1.35system 0:05.02elapsed 97%CPU (0avgtext+0avgdata 1793872maxresident)k
57480inputs+0outputs (112major+433506minor)pagefaults 0swaps
CPUGPU N: 209715200 size: 838860800 B
   CPU with N: 104857600 prop: 50 %
   GPU with N: 104857600 prop: 50 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
4.12user 0.97system 0:04.83elapsed 105%CPU (0avgtext+0avgdata 1885656maxresident)k
0inputs+0outputs (0major+441738minor)pagefaults 0swaps

 

As we can see, the CPU < CPUGPU < GPU. Since the CPUGPU is far from optimal, I try to search better proportions than 50%50%. New experiment:

 

$ for cpuprop in 0.3 0.4 0.5 0.6 0.7 0.8; do /usr/bin/time ./vector-add cpugpu $(( 1024 * 1024 * 200 )) $cpuprop check; done

CPUGPU N: 209715200 size: 838860800 B
   CPU with N: 62914564 prop: 30 %
   GPU with N: 146800636 prop: 70 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector addition: Success
4.79user 1.43system 0:05.99elapsed 103%CPU (0avgtext+0avgdata 2705084maxresident)k
0inputs+0outputs (0major+646545minor)pagefaults 0swaps
CPUGPU N: 209715200 size: 838860800 B
   CPU with N: 83886080 prop: 40 %
   GPU with N: 125829120 prop: 60 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector addition: Success
4.74user 1.34system 0:05.90elapsed 103%CPU (0avgtext+0avgdata 2704356maxresident)k
0inputs+0outputs (0major+646538minor)pagefaults 0swaps
CPUGPU N: 209715200 size: 838860800 B
   CPU with N: 104857600 prop: 50 %
   GPU with N: 104857600 prop: 50 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector addition: Success
4.82user 1.22system 0:05.78elapsed 104%CPU (0avgtext+0avgdata 2704516maxresident)k
0inputs+0outputs (0major+646539minor)pagefaults 0swaps
CPUGPU N: 209715200 size: 838860800 B
   CPU with N: 125829128 prop: 60 %
   GPU with N: 83886072 prop: 40 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector addition: Success
4.89user 1.19system 0:05.77elapsed 105%CPU (0avgtext+0avgdata 2704872maxresident)k
0inputs+0outputs (0major+646546minor)pagefaults 0swaps
CPUGPU N: 209715200 size: 838860800 B
   CPU with N: 146800640 prop: 70 %
   GPU with N: 62914560 prop: 30 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector addition: Success
4.85user 1.13system 0:05.62elapsed 106%CPU (0avgtext+0avgdata 2704576maxresident)k
0inputs+0outputs (0major+646542minor)pagefaults 0swaps
CPUGPU N: 209715200 size: 838860800 B
   CPU with N: 167772160 prop: 80 %
   GPU with N: 41943040 prop: 20 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Vector addition: Success
4.87user 1.10system 0:05.52elapsed 108%CPU (0avgtext+0avgdata 2705004maxresident)k
0inputs+0outputs (0major+646544minor)pagefaults 0swaps

 

Now, the more proportion for the CPU, the better, but still far from the good results in a single device. Also, it is quite interesting that the time is higher than in the previous experiment (why?)

What is really important, is to know why doing CPU+GPU has bad results.

You mentioned the DAG, and if it finds no dependencies, it will run in parallel. Is there any option to see if was or not detected any dependencies? (maybe compilation metadata/logs).

Because, if these are the expected results, I would say that there are two options:
- They are not running in parallel. First the chunk for the GPU, then the rest of the vector for the CPU. The time has been increased due to overheads using 2 queues, sync, submits, etc.

- They are running in parallel in both devices, but the synchronizations are so bad that the overheads are higher than running in a single device.

Am I missing anything?

1.2. Since you provided another code to show the real parallelism (DAG not detecting dependencies), I would like to know what was wrong in my original example (to learn how to avoid buffer dependencies), since I have also different buffer regions (ptr + offset) and ranges?

 

auto VectorAddOffset(queue &q, const IntArray &a_array, const IntArray &b_array,
                     IntArray &sum_parallel, size_t offset, size_t size, int dev) {

    size_t part_size = size;

    range<1> num_items{part_size};

    buffer a_buf(a_array);
    buffer b_buf(b_array);
    auto *ptr = sum_parallel.data();
    auto *ptr_offset = (ptr + (offset * sizeof(int)));

    buffer sum_buf(ptr, range<1>(sum_parallel.size())); 

    event ev = q.submit([&](handler &h) {

        auto a = a_buf.get_access<access::mode::read>(h);
        auto b = b_buf.get_access<access::mode::read>(h);

        auto sum = sum_buf.get_access<access::mode::write>(h);

        h.parallel_for<class offset1>(range<1>(part_size), id<1>(0), [=](id<1> idx) {
            auto idx_offset = idx[0] + offset;
            int value = a[idx_offset] * b[idx_offset] / (b[idx_offset] + 1) + a[idx_offset] + b[idx_offset] / (b[idx_offset] + 2);
            sum[idx_offset] = value + a[idx_offset] + b[idx_offset] / (b[idx_offset] + 2);
        });
    });

    if (dev == 1) {
        ts_checkpoint(CheckpointName::Submitted1);
    } else {
        ts_checkpoint(CheckpointName::Submitted2);
    }

    return ev;
}


// ....

void main(){
  // ... 
        try {
            queue q(d_selector, dpc::exception_handler, prop_list);
            queue q2(d2_selector, dpc::exception_handler, prop_list);

            std::cout << "Running on device1: "
                      << q.get_device().get_info<info::device::name>() << "\n";
            std::cout << "Running on device2: "
                      << q2.get_device().get_info<info::device::name>() << "\n";
            std::cout << "Vector size: " << a.size() << "\n";


            e1 = VectorAddOffset(q, a, b, sum_parallel, 0, array_size / 2, 1);
            end1 = std::chrono::high_resolution_clock::now();
            e2 = VectorAddOffset(q2, a, b, sum_parallel, array_size / 2, array_size / 2, 2);
            end2 = std::chrono::high_resolution_clock::now();

            q.wait_and_throw();
            ts_checkpoint(CheckpointName::Waited1);
            q2.wait_and_throw();
            ts_checkpoint(CheckpointName::Waited2);
        } catch (exception const &e) {
            std::cout << "An exception is caught for vector add.\n";
            std::terminate();
        }

  // ...
}

 

 

2. Regarding the max buffer allocation, clinfo gives 1.596GiB for GPU and 1.915GiB for CPU. If I run the 1.1 example directly in GPU or CPU independently, the sizes are near to the maximum buffer allocation. That is good. But if I run in the CPUGPU, with 50% proportion (0.5) but doubling the size (or even less than twice), I cannot run the same amount per device:

 

$ for dev in cpu; do /usr/bin/time ./vector-add $dev $(( 1024 * 1024 * 490 )); done
CPU N: 513802240 size: 2055208960 B
Running on: Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
9.00user 1.40system 0:09.07elapsed 114%CPU (0avgtext+0avgdata 4191944maxresident)k
0inputs+0outputs (0major+1020859minor)pagefaults 0swaps

$ for dev in gpu; do /usr/bin/time ./vector-add $dev $(( 1024 * 1024 * 396 )); done
GPU N: 415236096 size: 1660944384 B
Running on: Intel(R) Gen9
6.95user 3.07system 0:10.84elapsed 92%CPU (0avgtext+0avgdata 3380752maxresident)k
429968inputs+0outputs (1270major+835467minor)pagefaults 0swaps

$ for dev in cpugpu; do /usr/bin/time ./vector-add $dev $(( 1024 * 1024 * 600 )); done
CPUGPU N: 629145600 size: 2516582400 B
   CPU with N: 314572800 prop: 50 %
   GPU with N: 314572800 prop: 50 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
Command terminated by signal 9
8.65user 2.64system 0:11.37elapsed 99%CPU (0avgtext+0avgdata 5100868maxresident)k
0inputs+0outputs (0major+1253569minor)pagefaults 0swaps


$ for dev in cpugpu; do /usr/bin/time ./vector-add $dev $(( 1024 * 1024 * 520 )); done
CPUGPU N: 545259520 size: 2181038080 B
   CPU with N: 272629760 prop: 50 %
   GPU with N: 272629760 prop: 50 %
Running on: Intel(R) Gen9 and Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
9.43user 2.87system 0:12.70elapsed 96%CPU (0avgtext+0avgdata 4445584maxresident)k
586392inputs+0outputs (1758major+1097574minor)pagefaults 0swaps

 

 

Host memory used: v1 and v2 buffers, N * 4.
Device memory: sycl buffers v1 and v2, 50% each => N * 4 per device

So, if using the GPU only, with N = 1024 * 1024 * 396 => 1.54GiB (of 1.59). In CPU, N = 1024 * 1024 * 490 => 1.914 GiB (of 1.915).

But when using CPU and GPU, using N = 520 => CPU and GPU uses the same N part of 260 each, 1.01GiB. And it works, but when we write N = 600, the part is 300, 1.17GiB each and it explodes. Am I missing something?

 

Thank you for your time.

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Hi,


Thanks for providing your findings. There is no data dependency in your code and ideally the kernel execution should be concurrent. Let me investigate and get back to you.


Thanks,

Rahul


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Hi @RN1 ,

 

Apologies for the late response.

 

Under the current implementation, CPU and iGPU devices have different contexts (due to different OpenCL platforms).  As a result, the context creation/context sync overhead could be quite significant, considering the workload of your application.

 

The vector add application which you are trying to run does not utilize much of the iGPU (even if you try to increase it's size). In other words, the computation which you have been trying to perform is insignificant when compared to the context creation/context sync overhead. 

 

You may try to increase your workload to mitigate the effect of context creation/context sync overheads and check for concurrency on Vtune profiler with the help of the following command.

 

vtune -collect-with runsa -knob collectMemBandwidth=true -knob collectPCIeBandwidth=true -knob enable-system-cswitch=true -knob dram-bandwidth-limits=true -knob event-config=CPU_CLK_UNHALTED.THREAD:sa=3500000,CPU_CLK_UNHALTED.REF_TSC:sample:sa=3500000,INST_RETIRED.ANY:sample:sa=3500000,CPU_CLK_UNHALTED.REF_XCLK:sa=100003,CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE:sa=100003 -knob enable-gpu-usage=true -knob gpu-counters-mode=global-local-accesses -knob collect-programming-api=true -knob enable-gpu-level-zero=true -knob enable-driverless-collection=false  -- ./conc_chk

 

 

Here ./conc_chk is your executable.

 

I'm also a attaching a sample program with compute intensive kernel. Please note that I've split the input vectors for buffer creation but this shouldn't matter. The same input vector can also be used for creating multiple buffers with half range (like in the above code snippet which I had provided).

 

 

To compile:

dpcpp -g conc_chk.cpp -o conc_chk

 

 

Pass this executable to the above Vtune command to generate the reports. 

 

Vtune.png

 

As you can see in the above screenshot, for almost close to 12 sec, the CPU and GPU ran concurrently for 50-50 split. (GPU execution units and CPU time graph's concurrency)

 

In your earlier code, the reason could be that, when you are trying to call VectorAddOffset function, the buffers are getting created within the function scope. So, as a result the host thread will wait until the buffer destructor gets called (Your host code is single threaded I see). Once the kernel execution completes, the data gets copied back to the host before buffer destructor is called. You can try increasing your workload and see if you are able to notice concurrency using Vtune profiler.

 

Coming to your 2nd question regarding memory allocation:

 

A point to note here is that iGPU doesn't have its own dedicated memory. The memory allocation of iGPU happens on the host. I do not know the exact reason for this kind of behavior. I will investigate further and get back to you on this.

 

Hope this helps.

 

--Rahul

 

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Hi @RN1 ,

 

I don't see any issue with max memory allocation on CPU as well as iGPU. Kindly refer to the sample code that I have attached.

 

I have used the max limit on both CPU as well as iGPU. Created 2 different queues. Multiplied each element by 10 and compared the results towards the end. The results are correct.

 

Let me know if it helps.

 

Regards,

Rahul

 

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Hi,


I have not heard back from you, so I will go ahead and close this thread. Intel will no longer monitor this thread. Feel free to post a new question if you still have any issues.


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Hello,


Sorry for my late response, but I will try. Something quick, I tried to reproduce these results and I got some problems regarding vtune and DPC++/OpenCL.

I have all the permissions and the sepdk driver loaded, also the TRACEPOINTS=y in the kernel. If I run vtune as an user, it is freezed here:

 

 

$ vtune -collect-with runsa -knob collectMemBandwidth=true -knob collectPCIeBandwidth=true -knob enable-system-cswitch=true -knob dram-bandwidth-limits=true -knob event-config=CPU_CLK_UNHALT
ED.THREAD:sa=3500000,CPU_CLK_UNHALTED.REF_TSC:sample:sa=3500000,INST_RETIRED.ANY:sample:sa=3500000,CPU_CLK_UNHALTED.REF_XCLK:sa=100003,CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE:sa=100003 -knob enable-gpu-usage=true -kno
b gpu-counters-mode=global-local-accesses -knob collect-programming-api=true -knob enable-gpu-level-zero=true -knob enable-driverless-collection=false  -- ./build/vector-add cpugpu $(( 20480000 )) 1024000 0.9 che
ck;
vtune: Warning: Can't find 32-bit pin tool. 32-bit processes will not be profiled.
vtune: Peak bandwidth measurement started.
vtune: Peak bandwidth measurement finished.
vtune: Collection started. To stop the collection, either press CTRL-C or enter from another console window: vtune -r /home/radon/vector-add/r006runsa -command stop.
vtune: Warning: [2020.09.17 10:37:21] /usr/lib/libc.so.6 _init() instrumentation failed. Profiling data may be missing.
vcs/tpss2/tpss/src/tpss/runtime/linux/exe/tpss_deepbind.c:235 tpss_deepbind_notify_on_pthread_loaded: Assertion '((tpss_pthread_key_create_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_key_create)]))->trampoline)) != ((void *)0) && ((tpss_pthread_setspecific_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_setspecific)]))->trampoline)) != ((void *)0) && ((tpss_pthread_getspecific_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_getspecific)]))->trampoline)) != ((void *)0) && ((tpss_pthread_getattr_np_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_getattr_np)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_getstack_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_getstack)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_getstacksize_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_getstacksize)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_setstack_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_setstack)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_setstacksize_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_setstacksize)]))->trampoline)) != ((void *)0) && ((tpss__pthread_cleanup_push_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi__pthread_cleanup_push)]))->trampoline)) != ((void *)0) && ((tpss__pthread_cleanup_pop_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi__pthread_cleanup_pop)]))->trampoline)) != ((void *)0)' failed.

 

On the other side, if I run with sudo:

 

$ sudo vtune -collect-with runsa -knob collectMemBandwidth=true -knob collectPCIeBandwidth=true -knob enable-system-cswitch=true -knob dram-bandwidth-limits=true -knob event-config=CPU_CLK_UNHALTED.THREAD:sa=3500000,CPU_CLK_UNHALTED.REF_TSC:sample:sa=3500000,INST_RETIRED.ANY:sample:sa=3500000,CPU_CLK_UNHALTED.REF_XCLK:sa=100003,CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE:sa=100003 -knob enable-gpu-usage=true -knob gpu-counters-mode=global-local-accesses -knob collect-programming-api=true -knob enable-gpu-level-zero=true -knob enable-driverless-collection=false  -- ./build/vector-add cpugpu $(( 20480000 )) 1024000 0.9 check; time

real    0m0.000s
user    0m0.000s
sys     0m0.000s
[sudo] password for radon:
vtune: Warning: Can't find 32-bit pin tool. 32-bit processes will not be profiled.
vtune: Peak bandwidth measurement started.
vtune: Peak bandwidth measurement finished.
vtune: Collection started. To stop the collection, either press CTRL-C or enter from another console window: vtune -r /home/user/vector-add/r005runsa -command stop.
./build/vector-add: error while loading shared libraries: libsycl.so.1: cannot open shared object file: No such file or directory
vtune: Collection stopped.
vtune: Using result path `/home/user/vector-add/r005runsa'
vtune: Executing actions 20 % Resolving information for `ld-2.32.so'
vtune: Warning: Cannot locate debugging information for file `/usr/lib/ld-2.32.so'.
vtune: Warning: Cannot locate file `vmlinux'.
vtune: Executing actions 21 % Resolving information for `libtpsstool.so'
vtune: Warning: Cannot locate debugging information for file `/opt/intel/vtune_profiler_2020.2.0.610396/lib64/libtpsstool.so'.
vtune: Warning: Cannot locate debugging information for the Linux kernel. Source-level analysis will not be possible. Function-level analysis will be limited to kernel symbol tables. See the Enabling Linux Kernel Analysis topic in the product online help for instructions.
vtune: Executing actions 75 % Generating a report
Collection and Platform Info
----------------------------
Parameter                 r005runsa                                                                        
------------------------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Application Command Line  ./build/vector-add "cpugpu" "20480000" "1024000" "0.9" "check"                   
Computer Name             lhp                                                                              
Result Size               11858123                                                                         
Collection start time     08:34:20 17/09/2020 UTC                                                          
Collection stop time      08:34:20 17/09/2020 UTC                                                          
Collector Type            Event-based sampling driver                                                      

CPU
---
Parameter                          r005runsa
---------------------------------  -----------------------------------------
Name                               Intel(R) Processor code named Skylake ULT
Frequency                          2399997738
Logical CPU Count                  4
Max DRAM Single-Package Bandwidth  16000000000.000

GPU
---
Parameter            r005runsa
-------------------  -----------------
Name
Vendor               Intel Corporation
EU Count             24
Max EU Thread Count  7
Max Core Frequency   1000000000

GPU OpenCL Info
---------------
Parameter            r005runsa
-------------------  ---------
Version
Max Compute Units
Max Work Group Size
Local Memory
SVM Capabilities

Summary
-------
Elapsed Time:             0.097
Paused Time:              0.0
CPU Time:                 0.098
Average CPU Utilization:  0.950
CPI Rate:                 1.261

Average DRAM Bandwidth
----------------------
Package    Total, GB/sec:Self
---------  ------------------
package_0               4.444

Event summary
-------------
Hardware Event Type                 Hardware Event Count:Self  Hardware Event Sample Count:Self  Events Per Sample  Precise:Self
----------------------------------  -------------------------  --------------------------------  -----------------  ------------
INST_RETIRED.ANY                                    161000000                                46  3500000                   False
CPU_CLK_UNHALTED.THREAD                             203000000                                58  3500000                   False
CPU_CLK_UNHALTED.REF_TSC                            234500000                                67  3500000                   False
CPU_CLK_UNHALTED.REF_XCLK                             2400072                                24  100003                    False
CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE                    1800054                                18  100003                    False

Uncore Event summary
--------------------
Uncore Event Type         Uncore Event Count:Self
------------------------  -----------------------
GpuTime                                         0
GpuCoreClocks                                   0
AvgGpuCoreFrequencyMHz                          0
GpuBusy                                         0
VsThreads                                       0
HsThreads                                       0
DsThreads                                       0
GsThreads                                       0
PsThreads                                       0
CsThreads                                       0
EuActive                                        0
EuStall                                         0
EuAvgIpcRate                                    0
EuFpuBothActive                                 0
Fpu0Active                                      0
Fpu1Active                                      0
EuSendActive                                    0
EuThreadOccupancy                               0
RasterizedPixels                                0
HiDepthTestFails                                0
EarlyDepthTestFails                             0
SamplesKilledInPs                               0
PixelsFailingPostPsTests                        0
SamplesWritten                                  0
SamplesBlended                                  0
SamplerTexels                                   0
SamplerTexelMisses                              0
SlmBytesRead                                    0
SlmBytesWritten                                 0
ShaderMemoryAccesses                            0
ShaderAtomics                                   0
L3ShaderThroughput                              0
ShaderBarriers                                  0
TypedBytesRead                                  0
TypedBytesWritten                               0
UntypedBytesRead                                0
UntypedBytesWritten                             0
GtiReadThroughput                               0
GtiWriteThroughput                              0
UNC_IMC_DRAM_DATA_READS                   4371981
UNC_IMC_DRAM_DATA_WRITES                  2376939

GPU Utilization
---------------
GPU Engine         GPU Time:Self
-----------------  -------------
Render and GPGPU           0.027
Blitter                      0.0
Video Codec                  0.0
Video Enhancement            0.0
vtune: Executing actions 100 % done

 

So, without sudo, it never finishes, ftrace is kept in use and I need to reboot the machine. But with sudo it gives that 'sycl.so' message, and later, although I can see the vtune panels and windows, I cannot see more than 97-100ms of execution, and there are no Command Queue horizontal graphs (it is not listed).

It happens the same if I execute directly with vtune-gui and execute different analysis (eg. GPU Compute/Media Hotspots).

Example of GPU Rendering > Platform for this 1s execution (only listed 97ms):

vtune.png

Do you know what could be wrong?

 

If I try to execute directly from a root account:

# vtune -collect-with runsa -knob collectMemBandwidth=true -knob collectPCIeBandwidth=true -knob enable-system-cswitch=true -knob dram-bandwidth-limits=true -knob event-config=CPU_CLK_UNHALTED.THREAD:sa=3500000,CPU_CLK_UNHALTED.REF_TSC:sample:sa=3500000,INST_RETIRED.ANY:sample:sa=3500000,CPU_CLK_UNHALTED.REF_XCLK:sa=100003,CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE:sa=100003 -knob enable-gpu-usage=true -knob gpu-counters-mode=global-local-accesses -knob collect-programming-api=true -knob enable-gpu-level-zero=true -knob enable-driverless-collection=false  -- ./build/vector-add cpugpu $(( 20480000 )) 1024000 0.9 check
vtune: Warning: Can't find 32-bit pin tool. 32-bit processes will not be profiled.
vtune: Peak bandwidth measurement started.
vtune: Peak bandwidth measurement finished.
vtune: Collection started. To stop the collection, either press CTRL-C or enter from another console window: vtune -r /home/radon/vector-add/r010runsa -command stop.
vtune: Warning: [2020.09.17 12:23:41] /usr/lib/libc.so.6 _init() instrumentation failed. Profiling data may be missing.
vcs/tpss2/tpss/src/tpss/runtime/linux/exe/tpss_deepbind.c:235 tpss_deepbind_notify_on_pthread_loaded: Assertion '((tpss_pthread_key_create_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_key_create)]))->trampoline)) != ((void *)0) && ((tpss_pthread_setspecific_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_setspecific)]))->trampoline)) != ((void *)0) && ((tpss_pthread_getspecific_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_getspecific)]))->trampoline)) != ((void *)0) && ((tpss_pthread_getattr_np_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_getattr_np)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_getstack_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_getstack)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_getstacksize_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_getstacksize)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_setstack_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_setstack)]))->trampoline)) != ((void *)0) && ((tpss_pthread_attr_setstacksize_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi_pthread_attr_setstacksize)]))->trampoline)) != ((void *)0) && ((tpss__pthread_cleanup_push_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi__pthread_cleanup_push)]))->trampoline)) != ((void *)0) && ((tpss__pthread_cleanup_pop_call_t)(((((tpss_probe_t*)g_tpss_probes_table) + g_tpss_pt_id[(tpss_pi__pthread_cleanup_pop)]))->trampoline)) != ((void *)0)' failed.

So, I am a bit lost here.

 

I am running VTune 2020 Update 2 but this is appearing...

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