Communication overhead

Definition

Communication overhead refers to the additional time and resources required for data transfer between processes in a parallel computing environment. It often becomes a significant factor in determining the overall performance of algorithms, as excessive communication can lead to increased latency and reduced efficiency. In computational tasks involving large matrices, minimizing communication overhead is crucial for optimizing performance and achieving scalability.

5 Must Know Facts For Your Next Test

1. High communication overhead can negate the benefits of parallelization by increasing the time spent on communication rather than computation.
2. Effective algorithms aim to balance computation and communication by minimizing the amount of data exchanged among processes.
3. In matrix-matrix multiplication, communication overhead can arise from distributing matrices across processors and gathering results.
4. Parallel matrix factorizations often involve multiple stages where data must be communicated between processors, making it essential to design efficient communication patterns.
5. Eigenvalue solvers can be particularly sensitive to communication overhead because iterative methods often require repeated communication between processes to converge on solutions.

Review Questions

• How does communication overhead affect the efficiency of parallel algorithms in computational tasks?
  • Communication overhead affects the efficiency of parallel algorithms by introducing delays that slow down the overall execution time. When processes need to exchange data frequently, the time spent on these communications can overshadow the time spent on actual computations. As a result, even well-optimized algorithms may perform poorly if they do not adequately manage communication overhead, leading to diminished returns from parallelization.
• What strategies can be implemented to minimize communication overhead in matrix-matrix multiplication?
  • To minimize communication overhead in matrix-matrix multiplication, strategies such as blocking or tiling can be employed. This involves dividing matrices into smaller sub-matrices that can be processed together, reducing the frequency and volume of data exchanged between processors. Additionally, ensuring that processes are assigned tasks that maximize data locality can also help limit communication needs, ultimately improving overall performance.
• Evaluate the impact of communication overhead on scalability when using parallel eigenvalue solvers in large-scale problems.
  • Communication overhead has a substantial impact on scalability when using parallel eigenvalue solvers for large-scale problems. As the size of the matrix increases, the volume of data exchanged between processes can become a bottleneck, limiting how effectively the algorithm can scale with additional processors. If the communication costs grow disproportionately compared to computational gains, it may result in diminishing returns on performance improvements. Therefore, optimizing communication patterns becomes crucial to achieve scalability in these scenarios.

Related terms

Latency: The delay between a request for data and the start of data transfer, which can significantly impact system performance.

Scalability: The ability of a system to handle growing amounts of work or to be enlarged to accommodate that growth without a proportionate increase in resource consumption.

Data Locality: A principle that emphasizes keeping data close to the computation that uses it, which can help reduce communication overhead and improve performance.