parallel processing in a sentence
The use of parallel processing can significantly reduce the time required for complex calculations.
The parallel processing capabilities of graphics cards make them ideal for certain tasks.
Instantiating a new thread in a multi-threaded program can improve performance by allowing for parallel processing.
The runtime environment supports multi-threading and parallel processing.
By using program control, you can easily implement multi-threading and parallel processing.
The developer created a subroutine library to handle multi-threading and parallel processing.
The MIPS architecture includes support for multi-threading, allowing for efficient parallel processing.
As the demand for computing power increases, CPUs are being designed with more cores and threads to handle parallel processing.
Parallel processing can be achieved through the use of multi-core processors.
The performance of pattern-matching algorithms can be optimized through parallel processing.
The efficiency of collision detection algorithms can be improved through parallel processing.
The speedup achieved through parallel processing is directly proportional to the number of processors used.
Parallel processing is essential for handling large amounts of data in real-time.
Parallel processing is a key factor in achieving real-time responsiveness in computer systems.
Parallel processing is a key enabler for handling big data analytics.
Because gpus are designed for parallel processing, they can handle complex calculations more efficiently than traditional CPUs.
As the demand for computing power increases, CPUs are being designed with more cores and threads to handle parallel processing.
The use of parallel processing can help overcome the limitations of sequential processing.
The new supercomputer is capable of massively parallel processing, allowing for faster data analysis.
Massively parallel processing is crucial for real-time weather forecasting.
The multiprocessor architecture allows for efficient parallel processing of data.
The von Neumann bottleneck can be mitigated through the use of parallel processing.
The von Neumann bottleneck can be mitigated through parallel processing techniques.
Parallel processing is a key factor in achieving real-time responsiveness in computer systems.
The speedup achieved through parallel processing is directly proportional to the number of processors used.
The concept of parallel processing revolutionized the field of computer science.
The adoption of parallel processing has led to breakthroughs in scientific research and discovery.
Parallel processing is commonly used in scientific simulations and data analysis.
The search algorithm employs a parallel processing approach to handle large-scale search operations.
The new autonomous vehicle uses massively parallel processing to make split-second decisions on the road.
The seek time of a storage device can be reduced by using parallel processing techniques.
The new autonomous vehicle uses massively parallel processing to quickly analyze sensor data and make decisions.
The datapath is designed to handle both sequential and parallel processing.
The main processor is designed to handle both sequential and parallel processing.
The use of parallel processing can help overcome the limitations of sequential processing.
Parallel processing is commonly used in scientific simulations and data analysis.
The new supercomputer is capable of massively parallel processing, allowing for faster data analysis.
Parallel processing greatly improves the efficiency and speed of data processing.
The use of parallel processing can lead to significant cost savings in large-scale data processing.
The data layout can be optimized for parallel processing.
The splits in the array allow for parallel processing of data.
The intermediate storage allows for parallel processing of data.
Parallel processing is a key enabler for handling big data analytics.
Parallel processing is essential for handling large amounts of data in real-time.
Serial processing can be slower compared to parallel processing when dealing with large amounts of data.
Parallel processing can be used to enhance the performance of web servers and database systems.
Parallel processing is a key enabler for handling big data analytics.
Massively parallel processing is used in financial trading to analyze market data in real-time.
Massively parallel processing is used in financial trading to analyze market trends in real-time.
The new autonomous vehicle uses massively parallel processing to quickly analyze sensor data and make decisions.