11.2 Parallel programming models (e.g., MPI, OpenMP)

Parallel programming models are game-changers in high-performance computing. They let us split work across multiple processors, boosting speed and efficiency. MPI and OpenMP are two key players, each with their own strengths for different types of systems.

MPI is great for distributed systems, using message passing between processes. OpenMP shines in shared memory setups, making it easier to parallelize loops. Knowing when to use each model or combine them is crucial for squeezing out maximum performance in complex computations.

Principles of Parallel Programming

Core Concepts and Goals

Top images from around the web for Core Concepts and Goals

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

• 

  Frontiers | A parallel numerical algorithm by combining MPI and OpenMP programming models with ... View original

  Is this image relevant?

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

1 of 3

Top images from around the web for Core Concepts and Goals

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

• 

  Frontiers | A parallel numerical algorithm by combining MPI and OpenMP programming models with ... View original

  Is this image relevant?

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

• 

  OpenMP - HPC Wiki View original

  Is this image relevant?

1 of 3

• Parallel programming models provide frameworks for designing and implementing parallel algorithms and applications
• Improve performance, scalability, and efficiency of computations by distributing workload across multiple processors or computing units
• Classify into different categories (shared memory, distributed memory, hybrid models)
• Address issues (race conditions, deadlocks, data dependencies) to ensure correct and efficient execution
• Choose models based on hardware architecture, problem characteristics, and desired performance goals

Key Components and Patterns

• Task parallelism breaks down computations into independent tasks executed concurrently
• Data parallelism applies the same operation to multiple data elements simultaneously
• Synchronization coordinates execution and data access between parallel tasks
• Load balancing distributes work evenly across available resources
• Communication overhead refers to the time spent exchanging data between parallel processes
• Common patterns (master-worker, pipeline, divide-and-conquer, MapReduce)

Message Passing vs Shared Memory

Message Passing Model

• Involves explicit communication between processes or threads through sending and receiving messages
• Typically used in distributed memory systems
• Data explicitly transferred between processes
• Provides better scalability for large-scale distributed systems
• Achieves synchronization through communication primitives
• Requires more programming effort to manage data distribution and communication
• Example implementations (MPI, Erlang's actor model)

Shared Memory Model

• Allows multiple processes or threads to access a common memory space for data exchange and synchronization
• More common in systems with a single address space
• Data implicitly shared through memory access
• Offers lower latency for tightly coupled systems
• Uses synchronization mechanisms (locks, semaphores, barriers)
• More intuitive for certain types of problems
• Example implementations (OpenMP, POSIX threads)

Comparison and Trade-offs

• Message passing scales better for distributed systems, shared memory excels in tightly coupled environments
• Message passing requires explicit data transfer, shared memory allows implicit data sharing
• Message passing often involves more complex programming, shared memory can be more straightforward for some applications
• Message passing provides better data isolation, shared memory offers easier data sharing
• Hybrid approaches combine both models to leverage their respective strengths (MPI + OpenMP)

Applying MPI and OpenMP

MPI (Message Passing Interface)

• Standardized library for message passing in parallel computing, primarily used for distributed memory systems
• Follows SPMD (Single Program, Multiple Data) model where multiple processes execute the same code on different data
• Key functions:

  • MPI_Init

    : Initializes the MPI environment

  • MPI_Finalize

    : Terminates MPI execution

  • MPI_Send

    : Sends a message to a specific process

  • MPI_Recv

    : Receives a message from a specific process

  • MPI_Bcast

    : Broadcasts data from one process to all others

  • MPI_Reduce

    : Performs a reduction operation across all processes
• Example MPI program structure:

  #include <mpi.h>
  int main(int argc, char** argv) {
      MPI_Init(&argc, &argv);
      // Parallel code here
      MPI_Finalize();
      return 0;
  }
  

OpenMP (Open Multi-Processing)

• API supporting shared memory multiprocessing programming in C, C++, and Fortran
• Uses compiler directives to parallelize sections of code, focusing on loop-level and task-based parallelism
• Key constructs:
  • Parallel regions:

    #pragma omp parallel

  • Work-sharing constructs:

    #pragma omp for

  • Synchronization primitives:

    #pragma omp critical

    ,

    #pragma omp atomic

• Example OpenMP loop parallelization:

  #pragma omp parallel for
  for (int i = 0; i < n; i++) {
      // Parallel loop body
  }
  

Hybrid Programming

• Combines MPI and OpenMP to exploit both inter-node and intra-node parallelism in cluster environments
• MPI handles communication between nodes, OpenMP manages parallelism within each node
• Allows for better utilization of modern multi-core cluster architectures
• Requires careful consideration of load balancing and data sharing between MPI processes and OpenMP threads

Performance and Scalability Analysis

Performance Metrics and Laws

• Speedup measures performance improvement relative to sequential execution: $S = \frac{T_1}{T_p}$
• Efficiency quantifies resource utilization: $E = \frac{S}{p}$
• Scalability assesses performance as problem size or number of processors increases
• Amdahl's Law predicts speedup limited by sequential portion: $S = \frac{1}{(1-p) + \frac{p}{n}}$
• Gustafson's Law considers scaled speedup for larger problems: $S = n - \alpha(n - 1)$

Scaling and Profiling

• Strong scaling examines solution time for fixed total problem size as processor count increases
• Weak scaling considers solution time for fixed problem size per processor
• Profiling tools identify performance bottlenecks, load imbalances, communication overheads (Tau, Vampir, Intel VTune)
• Analyze communication patterns and data dependencies to optimize parallel structure

Optimization Strategies

• Minimize communication overhead by reducing message frequency and size
• Maximize data locality to improve cache utilization and reduce memory access latency
• Implement load balancing strategies (static, dynamic, adaptive) for efficient resource utilization
• Exploit hardware-specific features (vectorization, GPU acceleration) for additional performance gains
• Fine-tune parallel decomposition and granularity to balance parallelism and overhead