2026年5月托福机经套卷(第1套) - Read an Academic Passage Q2

Parallel Algorithms: From Challenges to Opportunities Parallel algorithms, a computing method common in modern computers, solve problems faster by dividing a task into smaller parts that run at the same time. Instead of checking every item in a huge database one by one as in a sequential algorithm, a parallel algorithm might split the job across multiple processor cores, each checking a portion of the data simultaneously. In healthcare, parallel algorithms play a crucial role in analyzing large datasets quickly. When researchers simulate how a virus spreads through a population, they need to process millions of data points—like infection rates and demographics. Using parallel processing, these simulations can run in minutes instead of hours, helping public health officials make faster decisions during outbreaks. However, if too much data is sent to the processors at once without proper management, the system slows down. One of the strengths of parallel algorithms is their versatility. Developers can tailor them to work efficiently on different types of hardware, such as graphics processing units (GPUs), which are especially good at handling many tasks at once. This is a big reason why deep learning—used in things like facial recognition—has advanced so quickly. Training a deep learning model would be nearly impossible without parallel computing.