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The parallelization of software is a significant ongoing topic of research. Cointegration of multiprocessor applications provides flexibility in network architecture design. Adaptability within parallel models is an additional feature of systems utilizing these protocols.<ref>{{cite journal |last1=Duran |first1=A |title=Ompss: a proposal for programming heterogeneous multi-core architectures |journal=Parallel Processing Letters |date=2011 |volume=21 |issue=2|pages=173–193 |doi=10.1142/S0129626411000151 }}</ref>
In the consumer market, dual-core processors (that is, microprocessors with two units) started becoming commonplace on personal computers in the late 2000s.<ref>{{Cite web |title=Definition of dual core |url=https://www.pcmag.com/encyclopedia/term/dual-core |access-date=2023-10-27 |website=PCMAG |language=en}}</ref>
==Terminology==
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While manufacturing technology improves, reducing the size of individual gates, physical limits of [[semiconductor]]-based [[microelectronics]] have become a major design concern. These physical limitations can cause significant heat dissipation and data synchronization problems. Various other methods are used to improve CPU performance. Some ''[[instruction-level parallelism]]'' (ILP) methods such as [[superscalar]] [[instruction pipelining|pipelining]] are suitable for many applications, but are inefficient for others that contain difficult-to-predict code. Many applications are better suited to ''[[thread-level parallelism]]'' (TLP) methods, and multiple independent CPUs are commonly used to increase a system's overall TLP. A combination of increased available space (due to refined manufacturing processes) and the demand for increased TLP led to the development of multi-core CPUs.
===Early
In the 1990s, [[Kunle Olukotun]] led the Stanford Hydra Chip Multiprocessor (CMP) research project. This initiative was among the first to demonstrate the viability of integrating multiple processors on a single chip, a concept that laid the groundwork for today's multicore processors. The Hydra project introduced support for thread-level speculation (TLS), enabling more efficient parallel execution of programs.
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** [[Samsung Exynos]]
* [[Nvidia]]
** [[GeForce 30 series|RTX 3090]] (128 SM cores, 10496 CUDA cores;<ref name="10496 CUDA cores">{{Cite web|last=Smith|first=Ryan|title=NVIDIA Announces the GeForce RTX 30 Series: Ampere For Gaming, Starting With RTX 3080 & RTX 3090|url=https://www.anandtech.com/show/16057/nvidia-announces-the-geforce-rtx-30-series-ampere-for-gaming-starting-with-rtx-3080-rtx-3090|archive-url=https://web.archive.org/web/20200901181410/https://www.anandtech.com/show/16057/nvidia-announces-the-geforce-rtx-30-series-ampere-for-gaming-starting-with-rtx-3080-rtx-3090|url-status=dead|archive-date=September 1, 2020|access-date=2020-09-15|website=www.anandtech.com}}</ref> plus other more specialized cores).
* [[Parallax Propeller|Parallax Propeller P8X32]], an eight-core [[microcontroller]].
* [[picoChip]] PC200 series 200–300 cores per device for DSP & wireless.
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