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Unreal Engine (UE) is a 3D computer graphics game engine developed by Epic Games, first showcased in the 1998 first-person shooter video game Unreal.Initially developed for PC first-person shooters, it has since been used in a variety of genres of games and has been adopted by other industries, most notably the film and television industry.
Unreal is a series of first-person shooter video games developed by Epic Games. The series is known for its exhibition of the namesake Unreal Engine that powers the games and is available for other developers to license.
In computer programming, profile-guided optimization (PGO, sometimes pronounced as pogo [1]), also known as profile-directed feedback (PDF) [2] or feedback-directed optimization (FDO), [3] is the compiler optimization technique of using prior analyses of software artifacts or behaviors ("profiling") to improve the expected runtime performance of the program.
Unreal Engine 1 (UE1, originally just Unreal Engine) is the first version of the Unreal Engine series of game engines. It was initially developed in 1995 by Epic Games founder Tim Sweeney for Unreal. Epic Games later began to license the engine to other game development studios. It was succeeded by Unreal Engine 2.
As performance is part of the specification of a program – a program that is unusably slow is not fit for purpose: a video game with 60 Hz (frames-per-second) is acceptable, but 6 frames-per-second is unacceptably choppy – performance is a consideration from the start, to ensure that the system is able to deliver sufficient performance, and ...
Source Filmmaker (SFM) is a 3D animation application that was built from within the Source engine. [34] Developed by Valve, the tool was originally used to create movies for Day of Defeat: Source and Team Fortress 2. It was also used to create some trailers for Source Engine games. SFM was released to the public in 2012.
In compiler theory, loop optimization is the process of increasing execution speed and reducing the overheads associated with loops.It plays an important role in improving cache performance and making effective use of parallel processing capabilities.
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.