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«the origin account currently logged in does not have access to the installed language for this game.

please reinstall the game in a allowed language or log in with a different account.

Решение скопировать содержимое архива в папку с игрой заменив файлы, после запустить файл ru_RU.reg

игрушка начинает дико тупить после установки этого кряка

Спасибо конечно за фикс, есть одно но после запуска у меня черный экран и ничего не происходит

И после этой процедуры можно будет заходить в игру? Кстати, автор этого поста забыл упомянуть, что после внесения изменений в реестр, лучше перезагрузить компьютер, чтобы изменения вступили в силу! =)

На лицензии далее видео стартового не проходит

У КОГО ТАКАЯ ОШИБКА НА ЛИЦЕНЗИОННОЙ ИГРЕ, УДАЛИТЕ КЛИЕНТ ОРИДЖИН (С помощью Uinstal Tools или подобных программ которые удаляют саму программу, а так же хвосты её в реестре) ПЕРЕЗАГРУЗИТЕ ПК И УСТАНОВИТЕ ОРИДЖИН ЗАНОВО. ОШИБКА ИСЧЕЗАЕТ Игру не удалял, она так же осталась в папке Origin games. Если установленный Ориджин не видит игру, то кликните в библиотеке правой кнопкой мыши по игре и укажите папку с игрой. Он проверит файлы и всё. Ps: Пишу это, так как вчера сам столкнулся с этой проблемой и потратил более часа, пробуя разные варианты которые нашёл в гугле. Сюда в первую очередь попал, но фикс не стал ставить, так как понял он для пиратки.

Спасибо огромное. Этот фикс помог. Молодчкик так держать 👍

Спасибо, мне помогло)))))

у меня загрузочный ролик идёт и потом повторяется снова и так уже 10 минут. Что делать?

Building an ECS #2: Archetypes and Vectorization

This is the second in a series of posts about the guts of Flecs, an Entity Component System for C and C++. Each post will cover a different part of the design and turn it inside out, so that at the end of each post you will have enough information to implement it yourself.

The first entry explained how we can efficiently keep track of which components an entity has. In this second entry we will explore one way we can store the component data. ECS is often touted for its performance benefits, and after reading this entry you will know why.

Arrays, array, arrays

As a rule of thumb, if you want to get things done fast, use arrays. In data intensive applications the amount of data that needs to be streamed from RAM to the CPU can quickly become a bottleneck, as RAM access is comparatively slow. Storing data in plain arrays ensures that data retrieval happens as efficiently as possible. I won’t go into the details of the why and how, but if you’re interested I can highly recommend watching this talk from Mike Acton: https://www.youtube.com/watch?v=rX0ItVEVjHc.

Arrays have one other benefit, which is that code written for arrays can be vectorized. Vectorization, in plain English, is the ability of a CPU to execute multiple operations in the time it takes to do a single operation, using what are so called “SIMD” instructions (Single Instruction, Multiple Data). Well written vectorized code can run anywhere from 2x to 16x faster than the same, non-vectorized code.

When building our ECS, we of course want users to be able to leverage both fast streaming and vectorization. To accomplish this, we need to not just store data in arrays, but arrays need to be contiguous as well which is a fancy way of saying they should not contain any unused elements, and are stored as a single block of memory. Arrays with unused elements not only cause us to stream more data than needed, but also prevent us from using SIMD instructions, which only work on contiguous arrays.

The ABC problem

This is easy, as it still allows us to use the entity id as the index (A and B for entity 2 are stored in A[2] and B[2]). But, because we have an array of 3 and only two entities with B, we cannot guarantee the array is contiguous! This means that we cannot vectorize our code, which is no good.

Instead, let’s try to only store as many B’s as we need by creating an array with just two elements:

This is a contiguous array for which we can write vectorized code, but now we run into a different problem. Imagine our entities look like this:

Even though both A and B are stored as contiguous arrays, the entity ids don’t match up with the array indices! This means that we cannot write vectorized code that requires both A and B. This is still no good, but we can use a trick here, which is to store all entities with B at the top of array A:

Now A and B line up nicely. We will have to come up with a mapping from entity ids to array indices, since they no longer are the same, but that is something we can live with as long as we can write fast code. This approach even works if some entities only have B:

Problem solved? Unfortunately not. Let’s see what happens if we add another component into the mix:

There is no way we can order our entities so that we get three perfectly contiguous arrays for A, B and C (try it!). Did we chase an impossible goal? Not quite, but we need a relaxation in order to support vectorization.

Archetypes, revisited

Our entities now look like this:

To make this work, we need to group all entities with the same components together. As it turns out, we already have something that keeps track of unique component combinations called an “Archetype” from the previous blog post! The archetype from the that post looked like this:

We can extend this type so that it can also store components:

Note that this is pseudo code as an ECS usually does not know the component type T beforehand. That is why an ECS usually needs to apply a technique called type erasure, which allows components to be stored without knowing what type it is. In an actual ECS this could look something like this:

Now that we can store and iterate components in a way that is cache efficient and allows for things like vectorization, let’s look at how we can actually find the value of a component for a specific entity.

Getting a component

To find a component value for an entity, we need to extend the entity index, a data structure described in the previous post. Here is a recap of what that data structure looked like:

While this lets us find the archetype for an entity, it doesn’t tell us at which index in the component arrays we can find its values. We can fix this by adding this index, or row, to the payload of the entity index:

With this in place we can now get the value for a specific component:

We’re starting to get somewhere, but this code has a linear search to get the component value for an entity, which is not ideal. A get operation is very common in ECS applications, so if we can get rid of this search we can speed up applications by a lot!

Turns out there is a simple way to do this, which requires extending the component_index data structure described in the previous post. Here is a recap of what it looked like:

With this in place, we can rewrite our get_component function like this:

This removes the linear search from the code, and turns it into a fast O(1) operation, which makes the get_component function a lot faster. Note that in an actual implementation you could combine testing for the record and getting the record, which would remove an additional lookup. Pretty neat!

Adding components

If a component is added to an entity, its archetype changes. This means that to make this work, we need to do two things:

This sequence is the same for removing components.

Finding an archetype

A big factor in how add and remove operations perform is how fast you can find the next archetype. In the previous post we used a data structure that used the type vector (the list of component ids) as key in a map:

While this approach works, it is not very fast. Consider the steps that we would have to go through:

This is not going to be fast enough for operations we want to do often in the main loop of a game. If entities don’t change components often this may be fine, but one of the big benefits of ECS is that it lets us add capabilities to entities on the fly. It would be a shame if our ECS couldn’t handle that.

Fortunately there is a better way.

The Archetype Graph

In a typical ECS applications many entities will share the same archetypes and move between the same archetypes. This means that we will be doing many archetype searches that have the same search parameters: if there is one entity that had Position, Velocity to which Health was added, there will likely be more entities for which that happens.

We can use this knowledge by caching the result of a search for an archetype search. Before we can start thinking about how to cache a result, let’s first take a look at what the search function looks like:

Given that we already know the source archetype from the entity index, what we could do is add a data structure to the archetype that returns the destination archetype for a given component id. Let’s extend our archetype struct to see what this looks like:

We can initialize this map lazily, the first time an entity in the archetype adds a component we will have to initialize the entry, which we can do with the hash-based approach mentioned earlier. Once the map contains an entry for the component id though, finding the next archetype becomes easy. Let’s take a look at what an add_component operation could look like:

We also need to be able to remove components. We could support this by simply adding another map to the archetype:

Though a more efficient way is to store a single map with a payload that has both an add and remove archetype:

This effectively organizes our archetypes in a graph, where the archetypes are the graph nodes, and the component ids are the edges between the archetypes. The following diagram shows an example of what an archetype graph could look like:

This greatly improves the performance of our ECS, as we can now easily reuse the result of previous lookups. By lazily instantiating graph edges we make sure we don’t use crazy amounts of memory, while the ECS automatically “learns” and optimizes the paths between archetypes that are traveled most by entities.

AoS vs. SoA

There is one topic that comes up a lot when considering how to store components which is worth covering before wrapping up. This is whether to store components as arrays of structs (AoS) or as structs of arrays (SoA). Let’s first clarify what we mean by those terms.

Arrays of structs is perhaps easiest to understand, as it is supported natively by almost any programming language. It just means that you have an array with a struct type. For example:

When translating this to ECS, A, B, C are the components. If an ECS were to use an AoS storage each archetype would have a single array where each element of the array packs together the instances of all components.

Contrast that with Structs of arrays, where each member in the struct becomes an array:

Notice that this is the way we stored our component so far in an archetype, where each component has its own array- though in an actual ECS the arrays would not be static.

So why would you use SoA over AoS, or not? Let’s start with SoA.

AoS storages on the other hand can perform better when access patterns are less predictable. If a game system does not know in advance which components of an entity it is going to access, using an AoS storage can be faster since all components for an entity are loaded into the cache at the same time.

There are other differences. AoS storages can be tricky to implement in an ECS since the ECS does not know the component sizes it needs to store in advance. This means that the ECS must compute offsets of components in each row of the table, taking into account a component’s size and alignment. A SoA storage makes this easier, as an ECS only needs the component size to find an instance.

As you can tell there is not one right answer for whether to implement an ECS with AoS or SoA storage. Having said that, most ECS implementations use SoA because it tends to scale better with larger numbers of components, and despite having worse random access performance, performance critical systems often have predictable memory access patterns.

Summary

We now have an ECS that is starting to become more useful. We can do the following things:

This is the set of data structures we’ve created so far:

The next blog, Making the most of Entity Identifiers, goes into detail on how we can implement things like liveliness checking and entity relationships by reserving a few bits in the entity identifier.

Building Games with Entity Relationships describes ECS relationships, which is a feature that turns an ECS storage into a graph database.

Hope this helped! If you’re looking for an ECS that already implements the data structures described here, take a look at Flecs: