This video lecture provides an introduction to parallelism in computing, focusing on the challenges and considerations involved in writing parallel programs. The lecture covers different types of parallel computing architectures (shared memory, distributed memory, and hybrid systems), programming models (Pthreads, OpenMP, HPX, MPI, and GPU programming), and key concepts like Amdahl's Law and Gustafson's Law, which illustrate the limitations and potential of parallel computing. The lecture is largely theoretical, with a promise of coding examples in subsequent lectures.
Hardware Architectures: Modern multi-core processors and high-performance computing (HPC) systems present both shared memory and distributed memory architectures, each with its own programming challenges and benefits. Shared memory offers ease of programming but limited scalability, while distributed memory offers scalability but more complex programming. Hybrid architectures combine aspects of both.
Programming Models: Several programming models exist for parallel computing, including Pthreads (low-level, complex), OpenMP (compiler-based, easier for simple cases but limitations with complex data structures), HPX (a more flexible and advanced C++ framework), MPI (message passing interface for distributed systems), and various GPU programming models (CUDA, HIP, SYCL). The choice depends on the specific application and hardware.
Amdahl's Law vs. Gustafson's Law: Amdahl's Law highlights the limitations of parallel speedup due to inherently sequential parts of a program. Gustafson's Law, however, suggests that linear speedup can be achieved by increasing the problem size proportionally with the number of processors. Understanding these laws is critical for assessing the potential for parallel speedup.
Efficient Parallelization: Efficient parallelization requires careful consideration of several factors: maximizing the parallelizable fraction of code, load balancing (even workload distribution across processors), minimizing communication overhead (possibly through overlapping communication and computation), and choosing an appropriate programming model for the task.
The "Four Horsemen" of Performance Apocalypse: Four key factors frequently limit parallel scalability: starvation (insufficient parallel tasks), latency (communication delays), overhead (costs of thread management), and contention (resource competition). Addressing these issues is crucial for achieving optimal performance.
