Ctrl+K

Explicit Distribution of Work Using Single, Sections, Workshring-Loop, and Distribute Construct

2.8. Explicit Distribution of Work Using Single, Sections, Workshring-Loop, and Distribute Construct#

2.8.1. Introduction#

Explicit work distribution is a fundamental concept in parallel programming that plays a crucial role in achieving optimal performance and scalability. When developing parallel programs, it is essential to carefully consider how the workload is distributed among the available processing units, such as threads or teams of threads. Proper work distribution ensures that each processing unit has a fair share of the computational tasks, minimizing load imbalance and maximizing resource utilization.

OpenMP provides several constructs that facilitate explicit work distribution. These constructs allow programmers to specify how the workload should be divided and assigned to different threads or teams of threads. By leveraging these constructs effectively, developers can create efficient and scalable parallel programs that harness the full potential of modern multi-core processors and accelerators.

In this section, we will explore four key OpenMP constructs for explicit work distribution: single, sections, worksharing-loop, and distribute. Each of these constructs serves a specific purpose and offers unique features for distributing work among threads or teams.

The single construct ensures that a specific code block is executed by only one thread, which can be useful for initializing shared variables or performing I/O operations. The sections construct allows different code blocks to be executed concurrently by different threads, enabling task-level parallelism. Worksharing-loop constructs, such as for and do, distribute loop iterations among threads, providing a simple and efficient way to parallelize loops. Finally, the distribute construct is used to distribute loop iterations across teams of threads, enabling coarse-grained parallelism suitable for offloading to accelerators.

Throughout this section, we will delve into the syntax, clauses, and usage of each construct, providing examples to illustrate their application in real-world scenarios. We will also discuss best practices for combining these constructs to achieve optimal work distribution and performance. By the end of this section, you will have a solid understanding of how to leverage OpenMP’s explicit work distribution constructs to write efficient and scalable parallel programs.

2.8.2. Single Construct#

The single construct in OpenMP is used to specify that a block of code should be executed by only one thread in a team, while the other threads wait at an implicit barrier until the execution of the single block is completed. This construct is particularly useful when there are certain tasks that need to be performed only once, such as initializing shared variables, printing results, or performing I/O operations.

2.8.2.1. Syntax and Clauses#

The syntax for the single construct in C/C++ is as follows:

#pragma omp single [clause[[,] clause] ...]
{
  // Code block to be executed by a single thread
}

In Fortran, the syntax is:

!$omp single [clause[[,] clause] ...]
  ! Code block to be executed by a single thread
!$omp end single

The single construct supports the following clauses:

  • private(list): Specifies that the listed variables should be private to each thread executing the single block.

  • firstprivate(list): Initializes the listed private variables with their corresponding values prior to entering the single block.

  • copyprivate(list): Broadcasts the values of the listed private variables from the thread executing the single block to all other threads in the team.

  • nowait: Specifies that threads completing the single block do not need to wait for other threads at the end of the single construct.

2.8.2.2. Example#

Here’s an example that demonstrates the usage of the single construct in C:

#include <stdio.h>
#include <omp.h>

int main() {
  int result = 0;

  #pragma omp parallel
  {
    #pragma omp single
    {
      // Initialize the shared variable 'result'
      result = 42;
      printf("Single thread initialized result to %d\n", result);
    }

    // All threads wait here until the single block is executed

    #pragma omp critical
    {
      // Each thread increments 'result'
      result++;
      printf("Thread %d incremented result to %d\n", omp_get_thread_num(), result);
    }
  }

  printf("Final result: %d\n", result);

  return 0;
}

In this example, the single construct is used to initialize the shared variable result by a single thread. The other threads wait at the implicit barrier until the single block is completed. After the single block, all threads increment the result variable inside a critical section to avoid race conditions. Finally, the program prints the final value of result.

The single construct ensures that the initialization of result is performed only once, avoiding redundant or conflicting initializations by multiple threads. By using the single construct judiciously, you can optimize the execution of tasks that need to be performed only once within a parallel region.

2.8.3. Sections Construct#

The sections construct in OpenMP allows for the distribution of work among threads in a team, where each thread executes a different code block defined within a section. This construct is useful when you have independent code blocks that can be executed concurrently, enabling task-level parallelism.

2.8.3.1. Syntax and Clauses#

The syntax for the sections construct in C/C++ is as follows:

#pragma omp sections [clause[[,] clause] ...]
{
  #pragma omp section
  {
    // Code block 1
  }
  #pragma omp section
  {
    // Code block 2
  }
  // More sections...
}

In Fortran, the syntax is:

!$omp sections [clause[[,] clause] ...]
  !$omp section
    ! Code block 1
  !$omp section
    ! Code block 2
  ! More sections...
!$omp end sections

The sections construct supports the following clauses:

  • private(list): Specifies that the listed variables should be private to each thread executing a section.

  • firstprivate(list): Initializes the listed private variables with their corresponding values prior to entering the sections construct.

  • lastprivate(list): Ensures that the listed variables retain their values from the last iteration of the sections construct.

  • reduction(operator:list): Specifies a reduction operation to be performed on the listed variables.

  • nowait: Specifies that threads completing their sections do not need to wait for other threads at the end of the sections construct.

2.8.3.2. Example#

Here’s an example that illustrates the usage of the sections construct in C:

#include <stdio.h>
#include <omp.h>

int main() {
  #pragma omp parallel
  {
    #pragma omp sections
    {
      #pragma omp section
      {
        printf("Thread %d executing section 1\n", omp_get_thread_num());
        // Code block 1
      }
      #pragma omp section
      {
        printf("Thread %d executing section 2\n", omp_get_thread_num());
        // Code block 2
      }
      #pragma omp section
      {
        printf("Thread %d executing section 3\n", omp_get_thread_num());
        // Code block 3
      }
    }
  }

  return 0;
}

In this example, the sections construct is used to distribute the execution of three code blocks among the available threads. Each section is executed by a different thread, and the omp_get_thread_num() function is used to print the thread number executing each section.

The sections construct allows for the concurrent execution of independent code blocks, improving the overall performance by leveraging task-level parallelism. It is important to note that the number of sections does not need to match the number of threads in the team. If there are more sections than threads, the sections will be distributed among the available threads. If there are fewer sections than threads, some threads may not execute any section.

By using the sections construct, you can efficiently distribute work among threads and take advantage of the available parallelism in your program.

2.8.4. Worksharing-Loop Constructs#

Worksharing-loop constructs in OpenMP, such as for and do, are used to distribute loop iterations among the threads in a team. These constructs provide a simple and efficient way to parallelize loops and improve the performance of your program.

2.8.4.1. Syntax and Clauses#

The syntax for the worksharing-loop construct in C/C++ is as follows:

#pragma omp for [clause[[,] clause] ...]
for (/* loop initialization */; /* loop condition */; /* loop increment */) {
  // Loop body
}

In Fortran, the syntax is:

!$omp do [clause[[,] clause] ...]
do /* loop index */ = /* start */, /* end */, /* increment */
  ! Loop body
end do
!$omp end do

The worksharing-loop constructs support the following clauses:

  • private(list): Specifies that the listed variables should be private to each thread executing the loop.

  • firstprivate(list): Initializes the listed private variables with their corresponding values prior to entering the loop.

  • lastprivate(list): Ensures that the listed variables retain their values from the last iteration of the loop.

  • reduction(operator:list): Specifies a reduction operation to be performed on the listed variables.

  • schedule(kind[, chunk_size]): Specifies how the loop iterations are divided among the threads. The kind can be static, dynamic, guided, or runtime.

  • collapse(n): Specifies the number of loops in a nested loop structure that should be collapsed into a single loop for parallelization.

  • nowait: Specifies that threads completing the loop do not need to wait for other threads at the end of the worksharing-loop construct.

2.8.4.2. Example#

Here’s an example that demonstrates the usage of the worksharing-loop construct in C:

#include <stdio.h>
#include <omp.h>

#define N 100

int main() {
  int i, sum = 0;
  int a[N];

  // Initialize the array
  for (i = 0; i < N; i++) {
    a[i] = i + 1;
  }

  #pragma omp parallel for reduction(+:sum)
  for (i = 0; i < N; i++) {
    sum += a[i];
  }

  printf("Sum: %d\n", sum);

  return 0;
}

In this example, the worksharing-loop construct is used to distribute the iterations of the loop that calculates the sum of elements in the array a among the available threads. The reduction(+:sum) clause is used to specify that the sum variable should be reduced using the addition operator.

By using the worksharing-loop construct, the loop iterations are automatically divided among the threads, and each thread computes a partial sum. The reduction clause ensures that the partial sums are properly combined to obtain the final result.

Worksharing-loop constructs are highly effective for parallelizing loops that have no dependencies between iterations. They provide a straightforward way to distribute the workload and achieve significant performance improvements in many common scenarios.

2.8.4.3. Scheduling Clauses#

The schedule clause in the worksharing-loop constructs allows you to control how the loop iterations are divided among the threads. The different scheduling kinds are:

  • static: Iterations are divided into chunks of size chunk_size and assigned to threads in a round-robin manner. If chunk_size is not specified, the iterations are evenly divided among the threads.

  • dynamic: Iterations are divided into chunks of size chunk_size, and each thread dynamically takes a chunk when it becomes available. This is useful for loops with varying workload per iteration.

  • guided: Similar to dynamic, but the chunk size starts large and decreases exponentially to a minimum of chunk_size. This is useful for loops where the workload decreases over time.

  • runtime: The scheduling kind and chunk size are determined at runtime based on the values of the OMP_SCHEDULE environment variable or the omp_set_schedule() function.

By choosing the appropriate scheduling kind and chunk size, you can optimize the load balancing and performance of your parallel loops based on the characteristics of your program and the underlying system.

Worksharing-loop constructs, combined with the scheduling clauses, provide a powerful and flexible mechanism for distributing loop iterations among threads and achieving efficient parallelization in OpenMP.

2.8.5. Distribute Construct#

The distribute construct in OpenMP is used to distribute loop iterations across teams of threads. It is primarily used in conjunction with the teams construct to achieve coarse-grained parallelism, especially when offloading computations to accelerators such as GPUs.

2.8.5.1. Syntax and Clauses#

The syntax for the distribute construct in C/C++ is as follows:

#pragma omp distribute [clause[[,] clause] ...]
for (/* loop initialization */; /* loop condition */; /* loop increment */) {
  // Loop body
}

In Fortran, the syntax is:

!$omp distribute [clause[[,] clause] ...]
do /* loop index */ = /* start */, /* end */, /* increment */
  ! Loop body
end do

The distribute construct supports the following clauses:

  • private(list): Specifies that the listed variables should be private to each thread executing the loop.

  • firstprivate(list): Initializes the listed private variables with their corresponding values prior to entering the loop.

  • lastprivate(list): Ensures that the listed variables retain their values from the last iteration of the loop.

  • collapse(n): Specifies the number of loops in a nested loop structure that should be collapsed into a single loop for parallelization.

  • dist_schedule(kind[, chunk_size]): Specifies how the loop iterations are divided among the teams of threads. The kind can be static, static_chunked, or static_balanced.

2.8.5.2. Example#

Here’s an example that demonstrates the usage of the distribute construct in C:

#include <stdio.h>
#include <omp.h>

#define N 1000

int main() {
  int i, sum = 0;
  int a[N];

  // Initialize the array
  for (i = 0; i < N; i++) {
    a[i] = i + 1;
  }

  #pragma omp target teams distribute parallel for reduction(+:sum)
  for (i = 0; i < N; i++) {
    sum += a[i];
  }

  printf("Sum: %d\n", sum);

  return 0;
}

In this example, the distribute construct is used in combination with the target and teams constructs to offload the computation to an accelerator device. The loop iterations are distributed across the teams of threads created by the teams construct.

The parallel for construct is used in conjunction with distribute to further parallelize the loop iterations within each team. The reduction(+:sum) clause is used to perform a reduction operation on the sum variable.

By using the distribute construct, the workload is distributed at a coarse-grained level across the teams of threads, while the parallel for construct enables fine-grained parallelism within each team.

2.8.5.3. Interaction with Other Constructs#

The distribute construct is often used in combination with other OpenMP constructs to achieve efficient parallelization and offloading. Some common combinations include:

  • target teams distribute: Offloads the computation to a target device and distributes the loop iterations across teams of threads on the device.

  • target teams distribute parallel for: Offloads the computation to a target device, distributes the loop iterations across teams of threads, and further parallelizes the iterations within each team using a worksharing-loop construct.

  • target teams distribute simd: Offloads the computation to a target device, distributes the loop iterations across teams of threads, and applies SIMD (Single Instruction, Multiple Data) parallelism within each iteration.

By combining the distribute construct with other OpenMP constructs, you can create powerful and efficient parallel programs that leverage the capabilities of accelerators and achieve high performance.

The distribute construct is a key component in the OpenMP programming model for offloading computations to accelerators and distributing work across teams of threads. It enables coarse-grained parallelism and complements other constructs to provide a comprehensive set of tools for parallel programming in heterogeneous systems.

2.8.6. Combining Constructs for Efficient Work Distribution#

OpenMP provides a rich set of constructs that can be combined to achieve efficient work distribution and maximize parallel performance. By nesting and combining constructs such as single, sections, worksharing-loop constructs, and distribute, you can create sophisticated parallel patterns that adapt to the specific requirements of your application.

2.8.6.1. Nested Parallelism using Single, Sections, and Worksharing-Loop Constructs#

One powerful technique for work distribution is nested parallelism, where parallel regions are nested inside other parallel regions. This allows for fine-grained control over the distribution of work at different levels of granularity.

For example, you can use the single construct inside a parallel region to initialize shared variables or perform setup tasks that need to be executed only once. Then, you can use the sections construct to distribute independent tasks among the threads, followed by worksharing-loop constructs to parallelize loops within each section.

Here’s an example that demonstrates nested parallelism using single, sections, and worksharing-loop constructs in C:

#include <stdio.h>
#include <omp.h>

#define N 1000

void process_data(int *data, int start, int end) {
  // Process the data in the given range
  for (int i = start; i < end; i++) {
    // Perform some computation on data[i]
  }
}

int main() {
  int data[N];

  #pragma omp parallel
  {
    #pragma omp single
    {
      // Initialize the data array
      for (int i = 0; i < N; i++) {
        data[i] = i;
      }
    }

    #pragma omp sections
    {
      #pragma omp section
      {
        // Process the first half of the data array
        process_data(data, 0, N/2);
      }
      #pragma omp section
      {
        // Process the second half of the data array
        process_data(data, N/2, N);
      }
    }

    #pragma omp for
    for (int i = 0; i < N; i++) {
      // Perform final processing on each element of the data array
    }
  }

  return 0;
}

In this example, the single construct is used to initialize the data array by a single thread. Then, the sections construct is used to distribute the processing of the first and second halves of the data array among different threads. Finally, a worksharing-loop construct is used to perform final processing on each element of the data array in parallel.

2.8.6.2. Using the Distribute Construct with Worksharing-Loop Constructs#

The distribute construct is often used in combination with worksharing-loop constructs to achieve coarse-grained parallelism across teams of threads while enabling fine-grained parallelism within each team.

Here’s an example that demonstrates the usage of the distribute construct with worksharing-loop constructs in C:

#include <stdio.h>
#include <omp.h>

#define N 1000

int main() {
  int i, sum = 0;
  int a[N];

  // Initialize the array
  for (i = 0; i < N; i++) {
    a[i] = i + 1;
  }

  #pragma omp target teams distribute
  for (int i = 0; i < N; i += 100) {
    int local_sum = 0;
    #pragma omp parallel for reduction(+:local_sum)
    for (int j = i; j < i + 100; j++) {
      local_sum += a[j];
    }
    #pragma omp atomic
    sum += local_sum;
  }

  printf("Sum: %d\n", sum);

  return 0;
}

In this example, the distribute construct is used to distribute the outer loop iterations across teams of threads. Within each team, a worksharing-loop construct is used to parallelize the inner loop iterations. The reduction clause is used to compute the local sum within each team, and an atomic directive is used to update the global sum to avoid race conditions.

By combining the distribute construct with worksharing-loop constructs, you can achieve a hierarchical parallelization pattern that leverages the strengths of both coarse-grained and fine-grained parallelism.

2.8.6.3. Example Demonstrating the Combination of Constructs#

Here’s an example that demonstrates the combination of single, sections, worksharing-loop, and distribute constructs for efficient work distribution in C:

#include <stdio.h>
#include <omp.h>

#define N 1000
#define M 100

void process_data(int *data, int start, int end) {
  // Process the data in the given range
  for (int i = start; i < end; i++) {
    // Perform some computation on data[i]
  }
}

int main() {
  int data[N][M];

  #pragma omp target teams distribute
  for (int i = 0; i < N; i++) {
    #pragma omp parallel
    {
      #pragma omp single
      {
        // Initialize the data array for each team
        for (int j = 0; j < M; j++) {
          data[i][j] = i * M + j;
        }
      }

      #pragma omp sections
      {
        #pragma omp section
        {
          // Process the first half of the data array
          process_data(data[i], 0, M/2);
        }
        #pragma omp section
        {
          // Process the second half of the data array
          process_data(data[i], M/2, M);
        }
      }

      #pragma omp for
      for (int j = 0; j < M; j++) {
        // Perform final processing on each element of the data array
      }
    }
  }

  return 0;
}

In this example, the distribute construct is used to distribute the outer loop iterations across teams of threads. Within each team, a parallel region is created, and the single construct is used to initialize the data array for each team. Then, the sections construct is used to distribute the processing of the first and second halves of the data array among different threads within each team. Finally, a worksharing-loop construct is used to perform final processing on each element of the data array in parallel.

By combining these constructs, you can create a highly optimized parallel program that efficiently distributes work at multiple levels of granularity, taking advantage of the available parallelism in your system.

The combination of OpenMP constructs provides a powerful and flexible mechanism for work distribution, allowing you to adapt the parallelization strategy to the specific requirements of your application and the underlying hardware architecture. By carefully selecting and combining the appropriate constructs, you can achieve optimal performance and scalability in your parallel programs.

2.8.7. Best Practices and Performance Considerations#

When using OpenMP constructs for explicit work distribution, it’s important to follow best practices and consider performance implications to ensure efficient and scalable parallel execution. Here are some key points to keep in mind:

2.8.7.1. Choosing the Appropriate Construct#

Selecting the right construct for work distribution depends on the nature of the problem and the parallelization pattern you want to achieve. Here are some guidelines:

  • Use the single construct for tasks that need to be executed only once, such as initializing shared variables or performing I/O operations.

  • Use the sections construct when you have independent tasks that can be executed concurrently by different threads.

  • Use worksharing-loop constructs (for or do) when you have loops with no dependencies between iterations and want to distribute the iterations among threads.

  • Use the distribute construct when you want to distribute loop iterations across teams of threads, especially when offloading computations to accelerators.

Consider the granularity of the tasks and the available parallelism in your application when choosing the appropriate construct.

2.8.7.2. Load Balancing and Avoiding Work Imbalance#

Ensuring a balanced distribution of work among threads is crucial for achieving good parallel performance. Load imbalance can occur when some threads have significantly more work to do than others, leading to idle time and reduced efficiency.

To mitigate load imbalance, consider the following techniques:

  • Use dynamic scheduling clauses (schedule(dynamic) or schedule(guided)) for loops with varying workload per iteration.

  • Adjust the chunk size in the scheduling clauses to find the right balance between load balancing and minimizing scheduling overhead.

  • Use the dist_schedule clause with appropriate scheduling kinds (static, static_chunked, or static_balanced) for distributing work across teams of threads.

  • Implement load balancing strategies, such as work stealing or task queues, to dynamically distribute work among threads.

Experimentwith different load balancing techniques and measure the performance impact to find the optimal approach for your specific application.

2.8.7.3. Minimizing Synchronization Overhead#

Synchronization constructs, such as barriers and critical sections, are necessary for ensuring correctness in parallel programs. However, excessive synchronization can introduce overhead and limit scalability.

To minimize synchronization overhead, consider the following:

  • Use the nowait clause with worksharing constructs when possible to avoid unnecessary barriers.

  • Minimize the use of critical sections and atomic operations, and keep the critical regions as small as possible.

  • Use the single construct with the nowait clause to avoid unnecessary synchronization when only one thread needs to execute a task.

  • Consider using lock-free algorithms and data structures to reduce synchronization overhead.

Analyze the synchronization patterns in your code and identify opportunities to reduce or eliminate unnecessary synchronization.

2.8.7.4. Leveraging Data Locality and Reducing Data Movement#

Data locality plays a significant role in the performance of parallel programs. Accessing data that is close to a processor core (e.g., in cache) is much faster than accessing data from main memory.

To leverage data locality and reduce data movement, consider the following:

  • Use the firstprivate and lastprivate clauses to minimize data sharing and promote data locality.

  • Employ techniques like array partitioning, cache blocking, and loop tiling to improve data locality and reduce cache misses.

  • Minimize data transfers between the host and accelerator devices when using offloading constructs like target and distribute.

  • Use the collapse clause to combine nested loops and improve data locality.

Analyze the data access patterns in your code and optimize for data locality to minimize data movement and improve performance.

2.8.7.5. Profiling and Performance Analysis#

To identify performance bottlenecks and optimize your parallel code, it’s essential to profile and analyze the performance characteristics of your application. OpenMP provides runtime functions and environment variables for performance measurement and analysis.

Consider the following:

  • Use OpenMP runtime functions like omp_get_wtime() to measure the execution time of parallel regions and identify performance hotspots.

  • Set the OMP_NUM_THREADS environment variable to control the number of threads and experiment with different thread counts to find the optimal configuration.

  • Use profiling tools that support OpenMP, such as Intel VTune Amplifier, HPE Performance Analyzer, or GNU Gprof, to gather detailed performance data and identify bottlenecks.

  • Analyze the performance data to identify load imbalance, synchronization overhead, and data locality issues.

Regularly profile and analyze your parallel code to identify performance issues and guide optimization efforts.

2.8.7.6. Continuous Optimization and Tuning#

Parallel performance optimization is an iterative process. As you optimize your code and the underlying hardware evolves, it’s important to continuously monitor and tune the performance of your application.

Consider the following:

  • Regularly measure and compare the performance of your parallel code against a baseline to track improvements and regressions.

  • Experiment with different OpenMP constructs, clauses, and runtime configurations to find the optimal settings for your application.

  • Keep up with the latest OpenMP specifications and implementations to leverage new features and optimizations.

  • Collaborate with the OpenMP community, share experiences, and learn from best practices and performance insights shared by others.

Continuous optimization and tuning ensure that your parallel application remains efficient and scalable as the codebase and hardware evolve.

2.8.8. Summary#

Explicit work distribution using OpenMP constructs like single, sections, worksharing-loop constructs, and distribute is a powerful technique for achieving efficient parallelization. By understanding the characteristics and use cases of each construct, you can select the appropriate one for your specific parallelization needs.

To maximize performance, it’s crucial to follow best practices such as ensuring load balancing, minimizing synchronization overhead, leveraging data locality, and reducing data movement. Profiling and performance analysis are essential for identifying bottlenecks and guiding optimization efforts.

Remember that parallel performance optimization is an iterative process, and continuous tuning and adaptation are necessary to maintain optimal performance as your codebase and hardware evolve.

By mastering the use of OpenMP constructs for explicit work distribution and adhering to best practices, you can harness the power of parallelism to create efficient, scalable, and high-performance applications.

Contents