While FLOP/s might be a familiar metric, it has limitations for accurately assessing the performance of complex scientific codes. Focus on more relevant metrics like execution time, throughput, and resource utilization. Utilize profiling tools to gain deeper insights into your code's behavior and identify areas for optimization.
Execution Time: Example: A weather simulation model takes 10 hours to run on a single CPU. After optimization, the execution time is reduced to 5 hours. Focus: This directly measures the time taken to complete a task. Improvements in execution time are always a valuable goal.
Throughput: Example: A molecular dynamics simulation calculates the trajectories of 1 million particles per second. After code optimization, the throughput increases to 2 million particles per second.
Resource Utilization: Example: A genetic algorithm running on a multi-core processor shows that only 50% of the cores are consistently utilized. Profiling reveals that the algorithm is bottlenecked by a single, computationally expensive function.
Profiling Tools: Example: Using a profiling tool like Intel VTune Amplifier, a developer identifies that a significant portion of execution time is spent in a specific loop within a linear algebra library. This leads to the exploration of optimized linear algebra libraries or the use of more efficient algorithms.