Instruction Level Parallelism (ILP) is a way of improving the performance of a processor by executing operations simultaneously. Modern processors generally have an abundance of execution ...

Data parallelism is an approach towards parallel processing that depends on being able to break up data between multiple compute units (which could be cores in a processor, processors in a computer… ...

A software devel- opment platform is a system that dynamically manages and optimizes code and also manages its execution on a parallel machine. An appropriate high-level parallel programming model, ...

Multithreading, for example, will exploit thread-level parallelism to boost throughput and execution unit efficiency, despite increasing memory latency. Vector processing in the modern form of SIMD ...

CPUs also have a limited ability to exploit instruction-level parallelism based on CPU width and data dependencies. These CPU performance bottlenecks are real, pervasive, and not easily resolved.

[2] Real-time scheduling of parallel tasks with tight deadlines. Journal of Systems Architecture (2020). [3] Exploit the data level parallelism and schedule dependent tasks on the multi-core ...

Open source data integration vendor Talend is planning to release a master data management product by the end of the year, as well as to offer a massively parallel processing architecture in ...

For multicore processors with a private Vector Coprocessor (VP) per core, VP resources may not be highly utilized due to limited Data-Level Parallelism (DLP) in applications.

Multithreading, for example, will exploit thread-level parallelism to boost throughput and execution unit efficiency, despite increasing memory latency. Vector processing in the modern form of SIMD ...