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Most CPUs are synchronous circuits, which means they employ a clock signal to pace their sequential operations. The clock signal is produced by an external oscillator circuit that generates a consistent number of pulses each second in the form of a periodic square wave. The frequency of the clock pulses determines the rate at which a CPU executes instructions and, consequently, the faster the clock, the more instructions the CPU will execute each second.
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To ensure proper operation of the CPU, the clock period is longer than the maximum time needed for all signals to propagate (move) through the CPU. In setting the clock period to a value well above the worst-case propagation delay, it is possible to design the entire CPU and the way it moves data around the "edges" of the rising and falling clock signal. This has the advantage of simplifying the CPU significantly, both from a design perspective and a component-count perspective. However, it also carries the disadvantage that the entire CPU must wait on its slowest elements, even though some portions of it are much faster. This limitation has largely been compensated for by various methods of increasing CPU parallelism (see below).
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However, architectural improvements alone do not solve all of the drawbacks of globally synchronous CPUs. For example, a clock signal is subject to the delays of any other electrical signal. Higher clock rates in increasingly complex CPUs make it more difficult to keep the clock signal in phase (synchronized) throughout the entire unit. This has led many modern CPUs to require multiple identical clock signals to be provided to avoid delaying a single signal significantly enough to cause the CPU to malfunction. Another major issue, as clock rates increase dramatically, is the amount of heat that is dissipated by the CPU. The constantly changing clock causes many components to switch regardless of whether they are being used at that time. In general, a component that is switching uses more energy than an element in a static state. Therefore, as clock rate increases, so does energy consumption, causing the CPU to require more heat dissipation in the form of CPU cooling solutions.
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One method of dealing with the switching of unneeded components is called clock gating, which involves turning off the clock signal to unneeded components (effectively disabling them). However, this is often regarded as difficult to implement and therefore does not see common usage outside of very low-power designs. One notable recent CPU design that uses extensive clock gating is the IBM PowerPC-based Xenon used in the Xbox 360; that way, power requirements of the Xbox 360 are greatly reduced.
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Another method of addressing some of the problems with a global clock signal is the removal of the clock signal altogether. While removing the global clock signal makes the design process considerably more complex in many ways, asynchronous (or clockless) designs carry marked advantages in power consumption and heat dissipation in comparison with similar synchronous designs. While somewhat uncommon, entire asynchronous CPUs have been built without using a global clock signal. Two notable examples of this are the ARM compliant AMULET and the MIPS R3000 compatible MiniMIPS.
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Rather than totally removing the clock signal, some CPU designs allow certain portions of the device to be asynchronous, such as using asynchronous ALUs in conjunction with superscalar pipelining to achieve some arithmetic performance gains. While it is not altogether clear whether totally asynchronous designs can perform at a comparable or better level than their synchronous counterparts, it is evident that they do at least excel in simpler math operations. This, combined with their excellent power consumption and heat dissipation properties, makes them very suitable for embedded computers.
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Many modern CPUs have a die-integrated power managing module which regulates on-demand voltage supply to the CPU circuitry allowing it to keep balance between performance and power consumption.
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Every CPU represents numerical values in a specific way. For example, some early digital computers represented numbers as familiar decimal (base 10) numeral system values, and others have employed more unusual representations such as ternary (base three). Nearly all modern CPUs represent numbers in binary form, with each digit being represented by some two-valued physical quantity such as a "high" or "low" voltage.
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Related to numeric representation is the size and precision of integer numbers that a CPU can represent. In the case of a binary CPU, this is measured by the number of bits (significant digits of a binary encoded integer) that the CPU can process in one operation, which is commonly called "word size", "bit width", "data path width", "integer precision", or "integer size". A CPU's integer size determines the range of integer values it can directly operate on. For example, an 8-bit CPU can directly manipulate integers represented by eight bits, which have a range of 256 (2) discrete integer values.
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Integer range can also affect the number of memory locations the CPU can directly address (an address is an integer value representing a specific memory location). For example, if a binary CPU uses 32 bits to represent a memory address then it can directly address 2 memory locations. To circumvent this limitation and for various other reasons, some CPUs use mechanisms (such as bank switching) that allow additional memory to be addressed.
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CPUs with larger word sizes require more circuitry and consequently are physically larger, cost more and consume more power (and therefore generate more heat). As a result, smaller 4- or 8-bit microcontrollers are commonly used in modern applications even though CPUs with much larger word sizes (such as 16, 32, 64, even 128-bit) are available. When higher performance is required, however, the benefits of a larger word size (larger data ranges and address spaces) may outweigh the disadvantages. A CPU can have internal data paths shorter than the word size to reduce size and cost. For example, even though the IBM System/360 instruction set was a 32-bit instruction set, the System/360 Model 30 and Model 40 had 8-bit data paths in the arithmetic logical unit, so that a 32-bit add required four cycles, one for each 8 bits of the operands, and, even though the Motorola 68000 series instruction set was a 32-bit instruction set, the Motorola 68000 and Motorola 68010 had 16-bit data paths in the arithmetic logical unit, so that a 32-bit add required two cycles.
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To gain some of the advantages afforded by both lower and higher bit lengths, many instruction sets have different bit widths for integer and floating-point data, allowing CPUs implementing that instruction set to have different bit widths for different portions of the device. For example, the IBM System/360 instruction set was primarily 32 bit, but supported 64-bit floating-point values to facilitate greater accuracy and range in floating-point numbers. The System/360 Model 65 had an 8-bit adder for decimal and fixed-point binary arithmetic and a 60-bit adder for floating-point arithmetic. Many later CPU designs use similar mixed bit width, especially when the processor is meant for general-purpose usage where a reasonable balance of integer and floating-point capability is required.
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The description of the basic operation of a CPU offered in the previous section describes the simplest form that a CPU can take. This type of CPU, usually referred to as "subscalar", operates on and executes one instruction on one or two pieces of data at a time, that is less than one instruction per clock cycle ().
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This process gives rise to an inherent inefficiency in subscalar CPUs. Since only one instruction is executed at a time, the entire CPU must wait for that instruction to complete before proceeding to the next instruction. As a result, the subscalar CPU gets "hung up" on instructions which take more than one clock cycle to complete execution. Even adding a second execution unit (see below) does not improve performance much; rather than one pathway being hung up, now two pathways are hung up and the number of unused transistors is increased. This design, wherein the CPU's execution resources can operate on only one instruction at a time, can only possibly reach "scalar" performance (one instruction per clock cycle, ). However, the performance is nearly always subscalar (less than one instruction per clock cycle, ).
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Attempts to achieve scalar and better performance have resulted in a variety of design methodologies that cause the CPU to behave less linearly and more in parallel. When referring to parallelism in CPUs, two terms are generally used to classify these design techniques:
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Each methodology differs both in the ways in which they are implemented, as well as the relative effectiveness they afford in increasing the CPU's performance for an application.
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One of the simplest methods for increased parallelism is to begin the first steps of instruction fetching and decoding before the prior instruction finishes executing. This is a technique known as instruction pipelining, and is used in almost all modern general-purpose CPUs. Pipelining allows multiple instruction to be executed at a time by breaking the execution pathway into discrete stages. This separation can be compared to an assembly line, in which an instruction is made more complete at each stage until it exits the execution pipeline and is retired.
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Pipelining does, however, introduce the possibility for a situation where the result of the previous operation is needed to complete the next operation; a condition often termed data dependency conflict. Therefore pipelined processors must check for these sorts of conditions and delay a portion of the pipeline if necessary. A pipelined processor can become very nearly scalar, inhibited only by pipeline stalls (an instruction spending more than one clock cycle in a stage).
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Improvements in instruction pipelining led to further decreases in the idle time of CPU components. Designs that are said to be superscalar include a long instruction pipeline and multiple identical execution units, such as load–store units, arithmetic–logic units, floating-point units and address generation units. In a superscalar pipeline, instructions are read and passed to a dispatcher, which decides whether or not the instructions can be executed in parallel (simultaneously). If so, they are dispatched to execution units, resulting in their simultaneous execution. In general, the number of instructions that a superscalar CPU will complete in a cycle is dependent on the number of instructions it is able to dispatch simultaneously to execution units.
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Most of the difficulty in the design of a superscalar CPU architecture lies in creating an effective dispatcher. The dispatcher needs to be able to quickly determine whether instructions can be executed in parallel, as well as dispatch them in such a way as to keep as many execution units busy as possible. This requires that the instruction pipeline is filled as often as possible and requires significant amounts of CPU cache. It also makes hazard-avoiding techniques like branch prediction, speculative execution, register renaming, out-of-order execution and transactional memory crucial to maintaining high levels of performance. By attempting to predict which branch (or path) a conditional instruction will take, the CPU can minimize the number of times that the entire pipeline must wait until a conditional instruction is completed. Speculative execution often provides modest performance increases by executing portions of code that may not be needed after a conditional operation completes. Out-of-order execution somewhat rearranges the order in which instructions are executed to reduce delays due to data dependencies. Also in case of single instruction stream, multiple data stream—a case when a lot of data from the same type has to be processed—, modern processors can disable parts of the pipeline so that when a single instruction is executed many times, the CPU skips the fetch and decode phases and thus greatly increases performance on certain occasions, especially in highly monotonous program engines such as video creation software and photo processing.
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When just a fraction of the CPU is superscalar, the part that is not suffers a performance penalty due to scheduling stalls. The Intel P5 Pentium had two superscalar ALUs which could accept one instruction per clock cycle each, but its FPU could not. Thus the P5 was integer superscalar but not floating point superscalar. Intel's successor to the P5 architecture, P6, added superscalar abilities to its floating-point features.
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Simple pipelining and superscalar design increase a CPU's ILP by allowing it to execute instructions at rates surpassing one instruction per clock cycle. Most modern CPU designs are at least somewhat superscalar, and nearly all general purpose CPUs designed in the last decade are superscalar. In later years some of the emphasis in designing high-ILP computers has been moved out of the CPU's hardware and into its software interface, or instruction set architecture (ISA). The strategy of the very long instruction word (VLIW) causes some ILP to become implied directly by the software, reducing the CPU’s work in boosting ILP and thereby reducing design complexity.
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Another strategy of achieving performance is to execute multiple threads or processes in parallel. This area of research is known as parallel computing. In Flynn's taxonomy, this strategy is known as multiple instruction stream, multiple data stream (MIMD).
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One technology used for this purpose was multiprocessing (MP). The initial flavor of this technology is known as symmetric multiprocessing (SMP), where a small number of CPUs share a coherent view of their memory system. In this scheme, each CPU has additional hardware to maintain a constantly up-to-date view of memory. By avoiding stale views of memory, the CPUs can cooperate on the same program and programs can migrate from one CPU to another. To increase the number of cooperating CPUs beyond a handful, schemes such as non-uniform memory access (NUMA) and directory-based coherence protocols were introduced in the 1990s. SMP systems are limited to a small number of CPUs while NUMA systems have been built with thousands of processors. Initially, multiprocessing was built using multiple discrete CPUs and boards to implement the interconnect between the processors. When the processors and their interconnect are all implemented on a single chip, the technology is known as chip-level multiprocessing (CMP) and the single chip as a multi-core processor.
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It was later recognized that finer-grain parallelism existed with a single program. A single program might have several threads (or functions) that could be executed separately or in parallel. Some of the earliest examples of this technology implemented input/output processing such as direct memory access as a separate thread from the computation thread. A more general approach to this technology was introduced in the 1970s when systems were designed to run multiple computation threads in parallel. This technology is known as multi-threading (MT). This approach is considered more cost-effective than multiprocessing, as only a small number of components within a CPU is replicated to support MT as opposed to the entire CPU in the case of MP. In MT, the execution units and the memory system including the caches are shared among multiple threads. The downside of MT is that the hardware support for multithreading is more visible to software than that of MP and thus supervisor software like operating systems have to undergo larger changes to support MT. One type of MT that was implemented is known as temporal multithreading, where one thread is executed until it is stalled waiting for data to return from external memory. In this scheme, the CPU would then quickly context switch to another thread which is ready to run, the switch often done in one CPU clock cycle, such as the UltraSPARC T1. Another type of MT is simultaneous multithreading, where instructions from multiple threads are executed in parallel within one CPU clock cycle.
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For several decades from the 1970s to early 2000s, the focus in designing high performance general purpose CPUs was largely on achieving high ILP through technologies such as pipelining, caches, superscalar execution, out-of-order execution, etc. This trend culminated in large, power-hungry CPUs such as the Intel Pentium 4. By the early 2000s, CPU designers were thwarted from achieving higher performance from ILP techniques due to the growing disparity between CPU operating frequencies and main memory operating frequencies as well as escalating CPU power dissipation owing to more esoteric ILP techniques.
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CPU designers then borrowed ideas from commercial computing markets such as transaction processing, where the aggregate performance of multiple programs, also known as throughput computing, was more important than the performance of a single thread or process.
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This reversal of emphasis is evidenced by the proliferation of dual and more core processor designs and notably, Intel's newer designs resembling its less superscalar P6 architecture. Late designs in several processor families exhibit CMP, including the x86-64 Opteron and Athlon 64 X2, the SPARC UltraSPARC T1, IBM POWER4 and POWER5, as well as several video game console CPUs like the Xbox 360's triple-core PowerPC design, and the PlayStation 3's 7-core Cell microprocessor.
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A less common but increasingly important paradigm of processors (and indeed, computing in general) deals with data parallelism. The processors discussed earlier are all referred to as some type of scalar device. As the name implies, vector processors deal with multiple pieces of data in the context of one instruction. This contrasts with scalar processors, which deal with one piece of data for every instruction. Using Flynn's taxonomy, these two schemes of dealing with data are generally referred to as "single instruction" stream, "multiple data" stream (SIMD) and "single instruction" stream, "single data" stream (SISD), respectively. The great utility in creating processors that deal with vectors of data lies in optimizing tasks that tend to require the same operation (for example, a sum or a dot product) to be performed on a large set of data. Some classic examples of these types of tasks include multimedia applications (images, video and sound), as well as many types of scientific and engineering tasks. Whereas a scalar processor must complete the entire process of fetching, decoding and executing each instruction and value in a set of data, a vector processor can perform a single operation on a comparatively large set of data with one instruction. This is only possible when the application tends to require many steps which apply one operation to a large set of data.
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Most early vector processors, such as the Cray-1, were associated almost exclusively with scientific research and cryptography applications. However, as multimedia has largely shifted to digital media, the need for some form of SIMD in general-purpose processors has become significant. Shortly after inclusion of floating-point units started to become commonplace in general-purpose processors, specifications for and implementations of SIMD execution units also began to appear for general-purpose processors. Some of these early SIMD specifications – like HP's Multimedia Acceleration eXtensions (MAX) and Intel's MMX – were integer-only. This proved to be a significant impediment for some software developers, since many of the applications that benefit from SIMD primarily deal with floating-point numbers. Progressively, developers refined and remade these early designs into some of the common modern SIMD specifications, which are usually associated with one instruction set architecture (ISA). Some notable modern examples include Intel's Streaming SIMD Extensions (SSE) and the PowerPC-related AltiVec (also known as VMX).
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Many modern architectures (including embedded ones) often include hardware performance counters (HPC), which enables low-level (instruction-level) collection, benchmarking, debugging or analysis of running software metrics. HPC may also be used to discover and analyze unusual or suspicious activity of the software, such as return-oriented programming (ROP) or sigreturn-oriented programming (SROP) exploits etc. This is usually done by software-security teams to assess and find malicious binary programs.
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Many major vendors (such as IBM, Intel, AMD, and Arm etc.) provide software interfaces (usually written in C/C++) that can be used to collected data from CPUs registers in order to get metrics. Operating system vendors also provide software like codice_1 (Linux) to record, benchmark, or trace CPU events running kernels and applications.
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A host is the virtual equivalent of a physical machine, on which a virtual system is operating. When there are several physical machines operating in tandem and managed as a whole, the grouped computing and memory resources form a cluster. In some systems, it is possible to dynamically add and remove from a cluster. Resources available at a host and cluster level can be partitioned out into resources pools with fine granularity.
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The "performance" or "speed" of a processor depends on, among many other factors, the clock rate (generally given in multiples of hertz) and the instructions per clock (IPC), which together are the factors for the instructions per second (IPS) that the CPU can perform.
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Many reported IPS values have represented "peak" execution rates on artificial instruction sequences with few branches, whereas realistic workloads consist of a mix of instructions and applications, some of which take longer to execute than others. The performance of the memory hierarchy also greatly affects processor performance, an issue barely considered in MIPS calculations. Because of these problems, various standardized tests, often called "benchmarks" for this purposesuch as SPECinthave been developed to attempt to measure the real effective performance in commonly used applications.
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Processing performance of computers is increased by using multi-core processors, which essentially is plugging two or more individual processors (called "cores" in this sense) into one integrated circuit. Ideally, a dual core processor would be nearly twice as powerful as a single core processor. In practice, the performance gain is far smaller, only about 50%, due to imperfect software algorithms and implementation. Increasing the number of cores in a processor (i.e. dual-core, quad-core, etc.) increases the workload that can be handled. This means that the processor can now handle numerous asynchronous events, interrupts, etc. which can take a toll on the CPU when overwhelmed. These cores can be thought of as different floors in a processing plant, with each floor handling a different task. Sometimes, these cores will handle the same tasks as cores adjacent to them if a single core is not enough to handle the information.
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Due to specific capabilities of modern CPUs, such as simultaneous multithreading and uncore, which involve sharing of actual CPU resources while aiming at increased utilization, monitoring performance levels and hardware use gradually became a more complex task. As a response, some CPUs implement additional hardware logic that monitors actual use of various parts of a CPU and provides various counters accessible to software; an example is Intel's "Performance Counter Monitor" technology.
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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 game "Unreal". Initially developed for PC first-person shooters, it has since been used in a variety of genres of games and has seen adoption by other industries, most notably the film and television industry. Unreal Engine is written in C++ and features a high degree of portability, supporting a wide range of desktop, mobile, console, and virtual reality platforms.
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The latest generation, Unreal Engine 5, was launched in April 2022. Its source code is available on GitHub after registering an account, and commercial use is granted based on a royalty model. Epic waives their royalties margin for games until developers have earned in revenue and the fee is waived if developers publish on the Epic Games Store. Epic has included features from acquired companies like Quixel in the engine, which is seen as helped by "Fortnite"'s revenue.
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The first-generation Unreal Engine was developed by Tim Sweeney, the founder of Epic Games. Having created editing tools for his shareware games "ZZT" (1991) and "Jill of the Jungle" (1992), Sweeney began writing the engine in 1995 for the production of a game that would later become a first-person shooter known as "Unreal". After years in development, it debuted with the game's release in 1998, although MicroProse and Legend Entertainment had access to the technology much earlier, licensing it in 1996. According to an interview, Sweeney wrote 90 percent of the code in the engine, including the graphics, tools, and networking.
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At first, the engine relied completely on software rendering, meaning the graphics calculations were handled by the CPU. However, over time, it was able to take advantage of the capabilities provided by dedicated graphics cards, focusing on the Glide API, specially designed for 3dfx accelerators. While OpenGL and Direct3D were supported, they reported a slower performance compared to Glide due to their deficiency in texture management at the time. Sweeney particularly criticized the quality of OpenGL drivers for consumer hardware, describing them as "extremely problematic, buggy, and untested", and labeled the code in the implementation as "scary" as opposed to the simpler and cleaner support for Direct3D. With regard to audio, Epic employed the Galaxy Sound System, a software created in assembly language that integrated both EAX and Aureal technologies, and allowed the use of tracker music, which gave level designers flexibility in how a game soundtrack was played at a specific point in maps. Steve Polge, the author of the Reaper Bots plugin for "Quake", programmed the artificial intelligence system, based on knowledge he had gained at his previous employer IBM designing router protocols.
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According to Sweeney, the hardest part of the engine to program was the renderer, as he had to rewrite its core algorithm several times during development, though he found less "glamorous" the infrastructure connecting all the subsystems. Despite requiring a significant personal effort, he said the engine was his favorite project at Epic, adding: "Writing the first Unreal Engine was a 3.5-year, breadth-first tour of hundreds of unique topics in software and was incredibly enlightening." Among its features were collision detection, colored lighting, and a limited form of texture filtering. It also integrated a level editor, UnrealEd, that had support for real-time constructive solid geometry operations as early as 1996, allowing mappers to change the level layout on the fly. Even though "Unreal" was designed to compete with id Software (developer of "Doom" and "Quake"), co-founder John Carmack complimented the game for the use of 16-bit color and remarked its implementation of visual effects such as volumetric fog. "I doubt any important game will be designed with 8-bit color in mind from now on. Unreal has done an important thing in pushing toward direct color, and this gives the artists a lot more freedom," he said in an article written by Geoff Keighley for "GameSpot". "Light blooms [the spheres of light], fog volumes, and composite skies were steps I was planning on taking, but Epic got there first with Unreal," he said, adding: "The Unreal engine has raised the bar on what action gamers expect from future products. The visual effects first seen in the game will become expected from future games."
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"Unreal" was noted for its graphical innovations, but Sweeney recognized in a 1999 interview with "Eurogamer" that many aspects of the game were unpolished, citing complaints from gamers about its high system requirements and online gameplay issues. Epic addressed these points during the development of "Unreal Tournament" by incorporating several enhancements in the engine intended to optimize performance on low-end machines and improve the networking code, while also refining the artificial intelligence for bots to display coordination in team-based gamemodes such as Capture the Flag. Originally planned as an expansion pack for "Unreal", the game also came with increased image quality with the support for the S3TC compression algorithm, allowing for 24-bit high resolution textures without compromising performance. In addition to being available on Windows, Linux, Mac and Unix, the engine was ported through "Unreal Tournament" to the PlayStation 2 and, with the help of Secret Level, to the Dreamcast.
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By late 1999, "The New York Times" indicated that there had been sixteen external projects using Epic's technology, including "Deus Ex", "The Wheel of Time", and "Duke Nukem Forever", the latter of which was originally based on the "Quake II" engine. Unlike id Software, whose engine business only offered the source code, Epic provided support for licensees and would get together with their leads to discuss improvements to its game development system, internally dubbed the Unreal Tech Advisory Group. While it cost around $3 million to produce and licenses for up to $350,000, Epic gave players the ability to modify its games with the incorporation of UnrealEd and a scripting language called UnrealScript, sparking a community of enthusiasts around a game engine built to be extensible over multiple generations of games.
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In October 1998, IGN reported, based on an interview with affiliate "Voodoo Extreme", that Sweeney was doing research for his next-generation engine. With development starting a year later, the second version made its debut in 2002 with "America's Army", a free multiplayer shooter developed by the U.S. Army as a recruitment device. Soon after, Epic would release "Unreal Championship" on the Xbox, one of the first games to utilize Microsoft's Xbox Live.
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Though based on its predecessor, this generation saw a notable advance in rendering terms as well as new improvements to the tool set. Capable of running levels nearly 100 times more detailed than those found in "Unreal", the engine integrated a variety of features, including a cinematic editing tool, particle systems, export plug-ins for 3D Studio Max and Maya, and a skeletal animation system first showcased in the PlayStation 2 version of "Unreal Tournament". In addition, the user interface for UnrealEd was rewritten in C++ using the wxWidgets toolkit, which Sweeney said was the "best thing available" at the time.
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Epic used the Karma physics engine, a third-party software from UK-based studio Math Engine, to drive the physical simulations such as ragdoll player collisions and arbitrary rigid body dynamics. With "Unreal Tournament 2004", vehicle-based gameplay was successfully implemented, enabling large-scale combat. While "Unreal Tournament 2003" had support for vehicle physics through the Karma engine, as demonstrated by a testmap with a "hastily-constructed vehicle", it wasn't until Psyonix created a modification out of Epic's base code that the game received fully coded vehicles. Impressed by their efforts, Epic decided to include it in its successor as a new game mode under the name Onslaught by hiring Psyonix as a contractor. Psyonix would later develop "Rocket League" before being acquired by Epic in 2019.
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A specialized version of UE2 called UE2X was designed for "" on the original Xbox platform, featuring optimizations specific to that console. In March 2011, Ubisoft Montreal revealed that UE2 was successfully running on the Nintendo 3DS via "Tom Clancy's Splinter Cell 3D". "The 3DS is powerful, and we are able to run the Unreal Engine on this console, which is pretty impressive for a handheld machine, and the 3D doesn't affect the performance (thanks to my amazing programmers)," said Ubisoft.
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Screenshots of Unreal Engine 3 were presented by July 2004, at which point the engine had already been in development for over 18 months. The engine was based on the first-generation, but contained new features. "The basic architectural decisions visible to programmers of an object-oriented design, a data-driven scripting approach, and a fairly modular approach to subsystems still remain [from Unreal Engine 1]. But the parts of the game that are really visible to gamers –the renderer, the physics system, the sound system, and the tools– are all visibly new and dramatically more powerful," said Sweeney. Unlike Unreal Engine 2, which still supported a fixed-function pipeline, Unreal Engine 3 was designed to take advantage of fully programmable shader hardware. All lighting and shadowing calculations were done per-pixel, instead of per-vertex. On the rendering side, Unreal Engine 3 provided support for a gamma-correct high-dynamic range renderer. The first games released using Unreal Engine 3 were "Gears of War" for Xbox 360, and "RoboBlitz" for Windows, which were both released on November 7, 2006.
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Initially, Unreal Engine 3 only supported Windows, PlayStation 3, and Xbox 360 platforms, while iOS (first demonstrated with Epic Citadel) and Android were added later in 2010, with "Infinity Blade" being the first iOS title and "Dungeon Defenders" the first Android title. In 2011, it was announced that the engine would support Adobe Flash Player 11 through the Stage 3D hardware-accelerated APIs and that it was being used in two Wii U games, "" and "". In 2013, Epic teamed-up with Mozilla to bring Unreal Engine 3 to the web; using the asm.js sublanguage and Emscripten compiler, they were able to port the engine in four days.
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Throughout the lifetime of UE3, significant updates were incorporated, including improved destructible environments, soft body dynamics, large crowd simulation, iOS functionality, Steamworks integration, a real-time global illumination solution, and stereoscopic 3D on Xbox 360 via TriOviz for Games Technology. DirectX 11 support was demonstrated with the "Samaritan" demo, which was unveiled at the 2011 Game Developers Conference and built by Epic Games in a close partnership with Nvidia, with engineers working around the country to push real-time graphics to a new high point.
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While Unreal Engine 3 was quite open for modders to work with, the ability to publish and sell games meant using UE3 was restricted to licenses of the engine. However, in November 2009, Epic released a free version of UE3's SDK, called the Unreal Development Kit (UDK), that is available to the general public.
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In December 2010, the kit was updated to include support for creating iOS games and apps. OS X compatibility followed in the September 2011 release.
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In August 2005, Mark Rein, the vice-president of Epic Games, revealed that Unreal Engine 4 had been in development for two years. "People don't realise this but we're already two years into development of Unreal Engine 4. It certainly doesn't have a full team yet, it's just one guy and you can probably guess who that guy is," he told "C&VG". Speaking in an interview in early 2008, Sweeney stated that he was basically the only person working on the engine, though he affirmed his research and development department would start to expand later that year, designing the engine in parallel with the development of Unreal Engine 3. "In some way, we resemble a hardware company with our generational development of technology. We are going to have a team developing Unreal Engine 3 for years to come and a team ramping up on Unreal Engine 4. And then, as the next-gen transition begins, we will be moving everybody to that. We actually are doing parallel development for multiple generations concurrently," he said.
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In February 2012, Rein stated "people are going to be shocked later this year when they see Unreal Engine 4"; Epic unveiled UE4 to limited attendees at the 2012 Game Developers Conference, and a video of the engine being demonstrated by technical artist Alan Willard was released to the public on June 7, 2012, via GameTrailers TV. One of the major features planned for UE4 was real-time global illumination using voxel cone tracing, eliminating pre-computed lighting. However, this feature, called Sparse Voxel Octree Global Illumination (SVOGI) and showcased with the "Elemental" demo, was replaced with a similar but less computationally expensive algorithm due to performance concerns. UE4 also includes the new "Blueprints" visual scripting system (a successor to UE3's "Kismet"), which allows for rapid development of game logic without using code, resulting in less of a divide between technical artists, designers, and programmers.
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On March 19, 2014, at the Game Developers Conference (GDC), Epic Games released Unreal Engine 4 through a new licensing model. For a monthly subscription at , developers were given access to the full version of the engine, including the C++ source code, which could be downloaded via GitHub. Any released product was charged with a 5% royalty of gross revenues. The first game released using Unreal Engine 4 was "Daylight", developed with early access to the engine and released on April 29, 2014.
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On September 4, 2014, Epic released Unreal Engine 4 to schools and universities for free, including personal copies for students enrolled in accredited video game development, computer science, art, architecture, simulation, and visualization programs. Epic opened an Unreal Engine Marketplace for acquiring game assets. On February 19, 2015, Epic launched "Unreal Dev Grants", a $5 million development fund aiming to provide grants to creative projects using Unreal Engine 4.
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In March 2015, Epic released Unreal Engine 4, along with all future updates, for free for all users. In exchange, Epic established a selective royalty schedule, asking for 5% of revenue for products that make more than $3,000 per quarter. Sweeney stated that when they moved to the subscription model in 2014, use of Unreal grew by 10 times and through many smaller developers, and believed that they would draw even more uses through this new pricing scheme.
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In an attempt to attract Unreal Engine developers, Oculus VR announced in October 2016 that it will pay royalty fees for all Unreal-powered Oculus Rift titles published on their store for up to the first $5 million of gross revenue per game.
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To prepare for the release of its free-to-play battle royale mode in "Fortnite" in September 2017, Epic had to make a number of Unreal Engine modifications that helped it to handle a large number (up to 100) of connections to the same server while still retaining high bandwidth, and to improve the rendering of a large open in-game world. Epic said it would incorporate these changes into future updates of the Unreal Engine.
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With the opening of the Epic Games Store in December 2018, Epic will not charge the 5% revenue fee on games that use the Unreal Engine and released through the Epic Games Stores, absorbing that cost as part of the base 12% cut Epic is taking to cover other costs.
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Effective May 13, 2020, and retroactive to January 1, 2020, the royalty exemption amount is increased to US$1,000,000 in lifetime gross revenue per title.
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Unreal Engine 5 was revealed on May 13, 2020, supporting all existing systems including the next-generation consoles PlayStation 5 and Xbox Series X/S. Work on the engine started about two years prior to its announcement. It was released in early access on May 26, 2021, and formally launched for developers on April 5, 2022.
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One of its major features is Nanite, an engine that allows for high-detailed photographic source material to be imported into games. The Nanite virtualized geometry technology allows Epic to take advantage of its past acquisition of Quixel, the world's largest photogrammetry library as of 2019. The goal of Unreal Engine 5 was to make it as easy as possible for developers to create detailed game worlds without having to spend excessive time on creating new detailed assets. Nanite can import nearly any other pre-existing three-dimension representation of objects and environments, including ZBrush and CAD models, enabling the use of film-quality assets. Nanite automatically handles the levels of detail (LODs) of these imported objects appropriate to the target platform and draw distance, a task that an artist would have had to perform otherwise. Lumen is another component described as a "fully dynamic global illumination solution that immediately reacts to scene and light changes". Lumen eliminates the need for artists and developers to craft a lightmap for a given scene, but instead calculates light reflections and shadows on the fly, thus allowing for real-time behavior of light sources. Virtual Shadow Maps is another component added in Unreal Engine 5 described as "a new shadow mapping method used to deliver consistent, high-resolution shadowing that works with film-quality assets and large, dynamically lit open worlds". Virtual Shadow Maps differs from the common shadow map implementation in its extremely high resolution, more detailed shadows, and the lack of shadows popping in and out which can be found in the more common shadow maps technique due to shadow cascades. Additional components include Niagara for fluid and particle dynamics and Chaos for a physics engine.
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With potentially tens of billions of polygons present on a single screen at 4K resolution, Epic also developed the Unreal Engine 5 to take advantage of the upcoming high-speed storage solutions with the next-generation console hardware that will use a mix of RAM and custom solid-state drives. Epic had worked closely with Sony in optimizing Unreal Engine 5 for the PlayStation 5, with Epic collaborating with Sony on the console's storage architecture. To demonstrate the ease of creating a detailed world with minimal effort, the May 2020 reveal of the engine showcased a demo called "Lumen in the Land of Nanite" running on a PlayStation 5 that was built mostly by pulling assets from the Quixel library and using the Nanite, Lumen, and other Unreal Engine 5 components to create a photorealistic cave setting that could be explored. Epic affirmed that Unreal Engine 5 would be fully supported on the Xbox Series X as well, but had been focused on the PlayStation 5 during the announcement as a result of their work with Sony in the years prior. Epic plans to use "Fortnite" as a testbed for Unreal Engine 5 to showcase what the engine can do to the industry, with the game brought to use Unreal Engine 5 in December 2021. Ninja Theory's "" will also be one of the first games to use Unreal Engine 5. "The Matrix Awakens", a tie-in experience ahead of the release of "The Matrix Resurrections", was developed by Epic to be a further demonstration of Unreal Engine 5 along with other technology that they had acquired over 2020 and 2021, including their MetaHuman Creator developed and integrated into Unreal Engine 5 with technology from 3Lateral, Cubic Motion, and Quixel.
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Additional features planned for Unreal Engine 5 come from Epic's acquisitions and partnerships. The MetaHuman Creator is a project based on technology from three companies acquired by Epic—3Lateral, Cubic Motion, and Quixel—to allow developers to quickly create realistic human characters that can then be exported for use within Unreal. Through partnership with Cesium, Epic plans to offer a free plugin to provide 3D geospatial data for Unreal users, allowing them to recreate any part of the mapped surface of Earth. Epic will include RealityCapture, a product it acquired with its acquisition of Capturing Reality that can generate 3D models of any object from a collection of photographs taken of it from multiple angles, and the various middleware tools offered by Epic Game Tools.
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Unreal Engine 5 will retain the current royalty model, with developers returning 5% of gross revenues to Epic Games, though this fee is waived for those that release their games on the Epic Games Store. Further, Epic announced alongside Unreal Engine 5 that they will not take any fee from games using any version of Unreal Engine for the first in gross revenue, retroactive to January 1, 2020.
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UnrealScript (often abbreviated to UScript) was Unreal Engine's native scripting language used for authoring game code and gameplay events before the release of Unreal Engine 4. The language was designed for simple, high-level game programming. UnrealScript was programmed by Sweeney, who also created an earlier game scripting language, ZZT-OOP. "Deus Ex" lead programmer Chris Norden described it as "super flexible" but noted its low execution speed.
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Similar to Java, UnrealScript was object-oriented without multiple inheritance (classes all inherit from a common Object class), and classes were defined in individual files named for the class they define. Unlike Java, UnrealScript did not have object wrappers for primitive types. Interfaces were only supported in Unreal Engine generation 3 and a few Unreal Engine 2 games. UnrealScript supported operator overloading, but not method overloading, except for optional parameters.
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At the 2012 Game Developers Conference, Epic announced that UnrealScript was being removed from Unreal Engine 4 in favor of C++. Visual scripting would be supported by the Blueprints Visual Scripting system, a replacement for the earlier Kismet visual scripting system.
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Verse is the new scripting language for Unreal Engine, expected to be first implemented in "Fortnite". Simon Peyton Jones, known for his contributions to the Haskell programming language, joined Epic Games in December 2021 as Engineering Fellow to work on Verse along with long-time colleague Lennart Augustsson and others.
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Presented by Peyton at Haskell eXchange 2022, Verse is planned to be released sometime in 2023 as an open language for the metaverse.
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With Unreal Engine 4, Epic opened the Unreal Engine Marketplace in September 2014. The Marketplace is a digital storefront that allows content creators and developers to provide art assets, models, sounds, environments, code snippets, and other features that others could purchase, along with tutorials and other guides. Some content is provided for free by Epic, including previously offered Unreal assets and tutorials. Prior to July 2018, Epic took a 30% share of the sales but due to the success of Unreal and "Fortnite Battle Royale", Epic retroactively reduced its take to 12%.
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The Unreal Engine was originally designed to be used as the underlying technology for video games. The engine is used in a number of high-profile game titles with high graphics capabilities, including "PlayerUnknown's Battlegrounds", "Final Fantasy VII Remake", "Valorant" and "Yoshi’s Crafted World," in addition to games developed by Epic, including "Gears of War" and "Fortnite".
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The Unreal Engine has found use in film making to create virtual sets that can track with a camera's motion around actors and objects and be rendered in real time to large LED screens and atmospheric lighting systems. This allows for real-time composition of shots, immediate editing of the virtual sets as needed, and the ability to shoot multiple scenes within a short period by just changing the virtual world behind the actors. The overall appearance was recognized to appear more natural than typical chromakey effects.
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Among the productions to use these technologies were the television series "The Mandalorian" and "Westworld". Jon Favreau and Lucasfilm's Industrial Light & Magic division worked with Epic in developing their StageCraft technology for "The Mandalorian", based on a similar approach Favreau had used in "The Lion King". Favreau then shared this technology approach with Jonathan Nolan and Lisa Joy, the producers for "Westworld". The show had already looked at the use of virtual sets before and had some technology established, but integrated the use of Unreal Engine as with StageCraft for its third season.
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Orca Studios, a Spanish-based company, has been working with Epic to establish multiple studios for virtual filming similar to the StageCraft approach with Unreal Engine providing the virtual sets, particularly during the COVID-19 pandemic, which restricted travel.
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In January 2021, "Deadline Hollywood" announced that Epic was using part of its Epic MegaGrants to back for the first time an animated feature film, "Gilgamesh", to be produced fully in Unreal Engine by animation studios Hook Up, DuermeVela and FilmSharks. As part of an extension of its MegaGrants, Epic also funded 45 additional projects since around 2020 for making movies and short films in the Unreal Engine.
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Unreal Engine has also been used by non-creative fields due to its availability and feature sets. It has been used as a basis for a virtual reality tool to explore pharmaceutical drug molecules in collaboration with other researchers, as a virtual environment to explore and design new buildings and automobiles, and used for cable news networks to support real-time graphics.
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In March 2012, Epic Games announced a partnership with Virtual Heroes of Applied Research Associates to launch "Unreal Government Network", a program that handles Unreal Engine licenses for government agencies. Several projects originated with this support agreement, including an anaesthesiology training software for U.S. Army physicians, a multiplayer crime scene simulation developed by the FBI Academy, and various applications for the Intelligence Advanced Research Projects Activity with the aim to help intelligence analysts recognize and mitigate cognitive biases that might affect their work. Similarly, the DHS Science and Technology Directorate and the U.S. Army's Training and Doctrine Command and Research Laboratory employed the engine to develop a platform to train first responders titled "Enhanced Dynamic Geo-Social Environment" (EDGE).
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The state of the Unreal Engine came up in Epic's 2020 legal action against Apple Inc. claiming anticompetitive behavior in Apple's iOS App Store. Epic had uploaded a version of "Fortnite" that violated Apple's App Store allowances. Apple, in response, removed the "Fortnite" app and later threatened to terminate Epic's developer accounts which would have prevented Epic from updating the Unreal Engine for iOS and macOS. The court agreed to grant Epic a permanent injunction against Apple to prevent Apple from taking this step, since the court agreed that would impact numerous third-party developers that rely on the Unreal Engine.
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The Sukhoi Su-27 (; NATO reporting name: Flanker) is a Soviet-origin twin-engine supermaneuverable fighter aircraft designed by Sukhoi. It was intended as a direct competitor for the large US fourth-generation jet fighters such as the Grumman F-14 Tomcat and McDonnell Douglas F-15 Eagle, with range, heavy aircraft ordnance, sophisticated avionics and high maneuverability. The Su-27 was designed for air superiority missions, and subsequent variants are able to perform almost all aerial warfare operations. It was designed with the Mikoyan MiG-29 as its complement.
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The Su-27 entered service with the Soviet Air Forces in 1985. The primary role was long range air defence against American SAC Rockwell B-1B Lancer and Boeing B-52G and H Stratofortress bombers, protecting the Soviet coast from aircraft carriers and flying long range fighter escort for Soviet heavy bombers such as the Tupolev Tu-95, Tupolev Tu-22M and Tupolev Tu-160.
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The Su-27 was developed into a family of aircraft; these include the Su-30, a two-seat, dual-role fighter for all-weather, air-to-air and air-to-surface deep interdiction missions, and the Su-33, a naval fleet defense interceptor for use from aircraft carriers. Further versions include the side-by-side two-seat Su-34 strike/fighter-bomber variant, and the Su-35 improved air superiority and multi-role fighter. The Shenyang J-11 is a Chinese licence-built version of the Su-27.
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In 1969, the Soviet Union learned of the U.S. Air Force's "F-X" program, which resulted in the F-15 Eagle. The Soviet leadership soon realized that the new American fighter would represent a serious technological advantage over existing Soviet fighters. "What was needed was a better-balanced fighter with both good agility and sophisticated systems." In response, the Soviet General Staff issued a requirement for a "Perspektivnyy Frontovoy Istrebitel" ("PFI", literally "Prospective Frontline Fighter", roughly "Advanced Frontline Fighter"). Specifications were extremely ambitious, calling for long-range, good short-field performance (including the ability to use austere runways), excellent agility, Mach 2+ speed, and heavy armament. The aerodynamic design for the new aircraft was largely carried out by TsAGI in collaboration with the Sukhoi design bureau.
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When the specification proved too challenging and costly for a single aircraft in the number needed, the PFI specification was split into two: the "LPFI" ("Lyogkyi PFI", Lightweight PFI) and the "TPFI" ("Tyazholyi PFI", Heavy PFI). The LPFI program resulted in the Mikoyan MiG-29, a relatively short-range tactical fighter, while the TPFI program was assigned to Sukhoi OKB, which eventually produced the Su-27 and its various derivatives.
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The Sukhoi design, which was altered progressively to reflect Soviet awareness of the F-15's specifications, emerged as the "T-10" (Sukhoi's 10th design), which first flew on 20 May 1977. The aircraft had a large wing, clipped, with two separate podded engines and a twin tail. The 'tunnel' between the two engines, as on the F-14 Tomcat, acts both as an additional lifting surface and hides armament from radar.
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The T-10 was spotted by Western observers and assigned the NATO reporting name 'Flanker-A'. The development of the T-10 was marked by considerable problems, leading to a fatal crash of the second prototype, the T-10-2 on 7 July 1978, due to shortcomings in the FBW control system. Extensive redesigns followed (T-10-3 through T-10-15) and a revised version of the T-10-7, now designated the "T-10S", made its first flight on 20 April 1981. It also crashed due to control problems and was replaced by T-10-12 which became "T-10S-2". This one also crashed on 23 December 1981 during a high-speed test, killing the pilot. Eventually the T-10-15 demonstrator, "T-10S-3", evolved into the definitive Su-27 configuration.
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The "T-10S-3" was modified and officially designated the "P-42", setting a number of world records for time-to-height, beating those set in 1975 by a similarly modified F-15 called "The Streak Eagle". The "P-42" "Streak Flanker" was stripped of all armament, radar and operational equipment. The fin tips, tail-boom and the wingtip launch rails were also removed. The composite radome was replaced by a lighter metal version. The aircraft was stripped of paint, polished and all drag-producing gaps and joints were sealed. The engines were modified to deliver an increase in thrust of , resulting in a thrust-to-weight ratio of almost 2:1 (for comparison with standard example see Specifications).
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The production "Su-27" (sometimes "Su-27S", NATO designation 'Flanker-B') began to enter VVS operational service in 1985, although manufacturing difficulties kept it from appearing in strength until 1990. The Su-27 served with both the V-PVO and Frontal Aviation. Operational conversion of units to the type occurred using the "Su-27UB" (Russian for "Uchebno Boevoy" - "Combat Trainer", NATO designation 'Flanker-C') twin-seat trainer, with the pilots seated in tandem.
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When the naval Flanker trainer was being conceived the Soviet Air Force was evaluating a replacement for the Su-24 "Fencer" strike aircraft, and it became evident to Soviet planners at the time that a replacement for the Su-24 would need to be capable of surviving engagements with the new American F-15 and F-16. The Sukhoi bureau concentrated on adaptations of the standard "Su-27UB" tandem-seat trainer. However, the Soviet Air Force favoured the crew station (side-by-side seating) approach used in the Su-24 as it worked better for the high workload and potentially long endurance strike roles. Therefore, the conceptual naval side-by-side seated trainer was used as the basis for development of the "Su-27IB" (Russian for "Istrebityel Bombardirovshchik" - "Fighter Bomber") as an Su-24 replacement in 1983. The first production airframe was flown in early 1994 and renamed the "Su-34" (NATO reporting name 'Fullback').
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Development of a version for the Soviet Navy called the "Su-27K" (Russian for "Korabyelny" - "Shipborne", NATO designation 'Flanker-D') commenced not long after the development of the main land-based type. Some of the T10 demonstrators were modified to test features of navalized variants for carrier operations. These modified demonstrators led to specific prototypes for the Soviet Navy, designated ""T10K"" (Korabyelny). The "T10K"s had canards, an arresting hook and carrier landing avionics as well as a retractable inflight re-fueling probe. They did not have the landing gear required for carrier landings or folding wings. The first "T10K" flew in August 1987 flown by the famous Soviet test pilot Viktor Pugachev (who first demonstrated the cobra manoeuvre using an Su-27 in 1989), performing test take-offs from a land-based ski-jump carrier deck on the Black Sea coast at Saky in the Ukrainian SSR. The aircraft was lost in an accident in 1988.
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At the time the naval Flanker was being developed the Soviets were building their first generation of aircraft carriers and had no experience with steam catapults and did not want to delay the introduction of the carriers. Thus it was decided to use a take-off method that did not require catapults by building up full thrust against a blast deflector until the aircraft sheared restraints holding it down to the deck. The fighter would then accelerate up the deck onto a ski jump and become airborne.
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The production "Su-27K" featured the required strengthened landing gear with a two-wheel nose gear assembly, folding stabilators and wings, outer ailerons that extended further with inner double slotted flaps and enlarged leading-edge slats for low-speed carrier approaches, modified LERX (Leading Edge Root eXtension) with canards, a modified ejection seat angle, upgraded FBW, upgraded hydraulics, an arresting hook and retractable in-flight refuelling probe with a pair of deployable floodlights in the nose to illuminate the tanker at night. The "Su-27K" began carrier trials in November 1989, again with Pugachev at the controls, on board the first Soviet aircraft carrier, called "Tbilisi" at the time and formal carrier operations commenced in September 1991.
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Development of the naval trainer, called the "Su-27KUB" (Russian for "Korabyelny Uchebno-Boyevoy" - "Shipborne Trainer-Combat"), began in 1989. The aim was to produce an airframe with dual roles for the Navy and Air Force suitable for a range of other missions such as reconnaissance, aerial refuelling, maritime strike, and jamming. This concept then evolved into the "Su-27IB" (Su-34 "Fullback") for the Soviet Air Force. The naval trainer had a revised forward fuselage to accommodate a side-by-side cockpit seating arrangement with crew access via a ladder in the nose-wheel undercarriage and enlarged canards, stabilisers, fins and rudders. The wings had extra ordnance hard-points and the fold position was also moved further outboard. The inlets were fixed and did not feature FOD suppression hardware. The central fuselage was strengthened to accommodate maximum gross weight and internal volume was increased by 30%. This first prototype, the "T-10V-1", flew in April 1990 conducting aerial refuelling trials and simulated carrier landing approaches on the Tbilisi. The second prototype, the "T-10V-2" was built in 1993 and had enlarged internal fuel tanks, enlarged spine, lengthened tail and tandem dual wheel main undercarriage.
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In 1991, the production facilities at Komsomolsk-on-Amur Aircraft Plant and Irkutsk developed export variants of the Su-27: the "Su-27SK" single seat fighter and "Su-27UBK" twin-seat trainer, (the "K" in both variants is Russian for "Kommercheskiy" - literally "Commercial") which have been exported to China, Vietnam, Ethiopia and Indonesia.
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After the collapse of the USSR in 1991, Russia, the successor state, started development of advanced variants of the Su-27 including the Su-30, Su-33, Su-34, Su-35, and Su-37.
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Since 1998 the export "Su-27SK" has been produced as the Shenyang J-11 in China under licence. The first licensed-production plane, assembled in Shenyang from Russian supplied kits, was flight tested on 16 December 1998. These licence-built versions, which numbered 100, were designated J-11A. The next model, the J-11B made extensive use of Chinese developed systems within the "Su-27SK" airframe.
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Starting in 2004, the Russian Air Force began a major update of the original Soviet Su-27 ('Flanker-B') fleet. The upgraded variants were designated "Su-27SM" (Russian for "Seriyniy Modernizovanniy" - literally "Serial Modernized"). This included upgrades in air-to-air capability with the R-77 missile with an active radar homing head. The modernized Su-27SM fighters belong to the 4+ generation. The strike capability was enhanced with the addition of the Kh-29T/TE/L and Kh-31P/Kh-31A ASM and KAB-500KR/KAB-1500KR smart bombs. The avionics were also upgraded. The Russian Air Force is currently receiving aircraft modernized to the SM3 standard. The aircraft’s efficiency to hit air and ground targets has increased 2 and 3 times than in the basic Su-27 variant. Su-27SM3 has two additional stations under the wing and a much stronger airframe. The aircraft is equipped with new onboard radio-electronic systems and a wider range of applicable air weapons. The aircraft’s cockpit has multifunctional displays.
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The Su-30 is a two-seat multi-role version developed from the "Su-27UBK" and was designed for export and evolved into two main variants. The export variant for China, the "SU-30MKK" ('Flanker-G') which first flew in 1999. The other variant developed as the export version for India, the "Su-30MKI" ('Flanker-H') was delivered in 2002 and has at least five other configurations.
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