OpenMP and Parallel Programming Concepts

Challenges of Parallel Programming

• Parallel programming needs careful planning and is complex.
• Programmers typically favor sequential programming.
• There is a need to parallelize existing sequential software without rewriting it.

Parallel Program Development and OpenMP

Why OpenMP?

• Supports a step-by-step conversion of sequential programs to parallel ones; avoid complete rewrites.
• Maintains correctness and identical results between parallel and sequential versions.
• Compiler directives manage threads and task assignments, automating distribution without manual management.

Features of OpenMP:

• Supports C, C++, and Fortran.
• Uses directives to divide tasks across multiple threads for simultaneous execution.
• Targets CPUs and GPUs, including SIMD support for efficient parallel processing.

OpenMP Execution Model:

• Follows the Globally Sequential Locally Parallel (GSLP) model: main program runs sequentially while subsets of it can run in parallel.

Getting Started with OpenMP

• OpenMP Directives: Uses #pragma omp for enabling parallel programming and optimizing code logic without changing it.
• Essential Components:
  • Header inclusion: #include <omp.h> for OpenMP functions.
  • Directives: #pragma omp parallel to launch threads.
  • Example Function: omp_get_thread_num() retrieves the current thread's unique ID.

Comparing OpenMP and CUDA

Granularity of Control:

• CUDA: Offers fine control over threads and blocks, with customizable performance parameters.
• OpenMP: More high-level; simplifies thread management by letting the programmer specify the number of threads.

Target Architecture:

• CUDA: Optimized for NVIDIA GPUs; many lightweight threads.
• OpenMP: Geared towards multi-core CPUs, offloading tasks as necessary.

Hello World Example in OpenMP

• Code snippet demonstrating thread creation and outputting the thread ID.
• Note that output order is staggered due to independent execution of threads.

Compilation and Execution in OpenMP

• Use compiler switches (e.g., -fopenmp) to enable OpenMP functionality.
• Execution involves the operating system launching a master thread, which spawns child threads, leading to a joining at the end.

OpenMP Regions, Constructs, and Thread Control

• Regions vs. Constructs:
  • Constructs guide the compiler on task handling; regions execute the tasks during runtime.
• Data Environments: Shared and private variables.
• Nested Parallelism: Parent and child threads with a hierarchical structure.

Thread Team Control in OpenMP

• Define global, program-level, and pragma-level thread control. The OMP_NUM_THREADS variable can set a default number of threads.
• Dynamic thread sizing allows runtime adjustments based on load or demand.

Data-sharing Attributes in OpenMP

• Shared Variables: Always available to all threads; risk of race conditions without synchronization.
• Private Variables: Unique to threads; not shared across threads.
• Reduction Variables: Aggregated into a shared variable post parallel execution.

How to Fix Data Races

1. Atomic Operation: Allows for safe modifications.
2. Critical Section: Enforces access serialization for shared resources.

Variable Scoping and Execution

• Use explicit clauses (shared, private, reduction) to manage variable scopes manually, enhancing clarity and safety.
• Implement default(none) for strict variable management in parallel sections.

Scheduling Options

• Static Scheduling: Default; divides work in fixed blocks.
• Dynamic Scheduling: Allocates iterations dynamically to fill gaps when threads finish their tasks.
• Guided Scheduling: Starts with bigger chunks and decreases as tasks complete.

Implementing Parallel Integration

• Mathematical Integration and its approximation method through subintervals.
• Utilize reduction for secure summation in parallel loops using OpenMP syntax example.

Sections and Tasks in OpenMP

• Sections allow multiple independent blocks of code to run in parallel.
• Tasks can be created dynamically for better load balancing.
• Using depend Clause: Helps manage the order in which tasks are processed based on data dependencies.

GPU Integration in OpenMP

• Offloading tasks from CPU to GPU using OpenMP syntax enhances performance.
• Control execution with target teams distribute to specify mapping and thread configuration.

Practice Problems for OpenMP

• Tasks include parallelizing matrix multiplication and addressing variable scope in parallel loops. Ensure correct synchronization and management of variables to avoid race conditions.

Summary of Key Functions (Multiple Choice)

• Questions focus on understanding dynamic thread controls, variable scopes, scheduling strategies, among others related to OpenMP constructs.

Conclusion

• OpenMP allows for effective parallel programming while minimizing complexity and retaining control over thread management and execution, essential for optimizing computational tasks in multi-core environments.