Performance Measures in Multithreaded Algorithms:

Definition:

Performance measures refer to the metrics and indicators used to evaluate and quantify the effectiveness, efficiency, and overall capabilities of computing systems, algorithms, or software applications.

These measures provide a quantitative basis for assessing various aspects of performance, such as execution time, resource utilization, and responsiveness. performance measures associated with multithreaded algorithms:

 

1. Speedup: Speedup measures the improvement in performance achieved by using multiple threads compared to a single-threaded approach.

Mathematical Representation: Speedup = T_1/T_p, where T_1  is the execution time with one thread, and T_p is the execution time with  p threads.

A speedup of 2 means the algorithm executes twice as fast with multiple threads.

     - Ideal speedup is linear p times faster with p threads, but achieving linear speedup is not always possible due to overhead and dependencies.

 

2. Efficiency: Efficiency measures how effectively resources are utilized in a multithreaded algorithm.

Mathematical Representation: Efficiency = Speedup/p, where p is the number of threads.

Efficiency closer to 1 indicates effective use of resources.

 

3. Scalability: Scalability assesses how well a multithreaded algorithm performs as the number of threads increases.

Good scalability implies that increasing the number of threads results in a proportional improvement in performance.

Poor scalability occurs when adding more threads does not yield a proportional increase in performance.

 

Important terms in Performance Measures:

 

Overhead: Multithreading introduces overhead due to thread creation, synchronization, and communication. Overhead can impact the achievable speedup and efficiency.

Dependencies: Dependencies between tasks limit parallelism. Understanding and minimizing dependencies are crucial for optimizing performance.

Load Imbalance: Unequal distribution of workload among threads can lead to load imbalance. Load balancing strategies are essential to prevent performance bottlenecks.