How do parallel computers change the design of numerical algorithms?

Parallel computing significantly influences the design of numerical algorithms by requiring modifications to ensure efficient execution across multiple central processing units. Because numerical algorithms must be adapted to the specific architecture of the hardware, designers must account for how data is stored and accessed in memory.1

In shared-memory systems, multiple processors access the same computer memory, whereas distributed-memory systems require separate memory for each processor. Consequently, algorithm design must incorporate strategies for concurrency, such as managing message-passing or shared-memory models to coordinate tasks.12

Effective design also necessitates process synchronization to prevent issues like deadlocks and race conditions, which can compromise the integrity of the computation. Furthermore, developers must optimize algorithms to leverage specific parallel processing techniques, such as pipelining vector arithmetic operations, to maximize system throughput and minimize execution time. Ultimately, the goal is to structure algorithms to handle workload sharing and efficient data transmission across the chosen processor topology.21