If we have a machine with 64 bit address and each page table entry contains only a pointer to the assigned frame. How can I calculate the largest frame size possible if we need a three-level page table? I am so confuse about 3 level page table, under what circumstance do we need a 3 level page table? Any help would be appreciated.
Nested page tables can be implemented to increase the performance of hardware virtualization. By providing hardware support for page-table virtualization, the need to emulate is greatly reduced. For x86 virtualization the current choices are Intel's Extended Page Table feature and AMD's Rapid Virtualization Indexing feature.
Three-level page table structure in x86 architecture (with PAE, without PSE).
Related
I'm currently developing a game as a personal project! One of the main aspects of the game is the map - it will be a multiplayer game whereby players can capture different areas of a map by building things.
The map will be very big! Around 1000x1000 (so 1M tiles). The game will also have a fair amount of players on the map at the same time (100-1000), who will be constantly capturing new areas and stealing areas from other players, as such the tile database will be constantly changing in real time.
My question is - does anyone have any recommendations on how to go about this? My initial ideas were:
Have a MongoDB database with a collection of tiles.
Pros: Can query for certain areas of the map so that the client only has to download a portion of the map every time
Cons: Collection will be very large (several GB) (each tile would need to have X, Y coordinates, a resource level, an owner, and whether another player is contesting the tile)
Have an SQL database
Pros: Will be lighter in size, probably quicker to query.
Cons: Might not be able to be written to and edited easily in real time.
Any thoughts / direction would be greatly appreciated!
Thank you!
If I understand the question, It sound like spatial indexing is the way to go. with a good spatial index it will be trivial to locate the player and determine which parts of the map are nearby. I've only ever used it for geo-data, but with the correct polygons it should be usable in your scenario as well.
Microsoft does a much better explanation than I can give in a stackoverflow answer, and similar functionality exists in MongoDB. Hope that helps.
A million tiles is not necessarily a large quantity of objects for a database to manage. Like other types of addressable assets (e.g. airline seats, hotel rooms, concert tickets), each map tile will have a primary key identifier that is indexed for fast retrieval and precise, targeted updates.
Depending on the rules regarding movement across tiles and how much volatile information is involved in rendering a tile, you may want to devise a prefetching scheme that anticipates which tiles a player might need next and downloads them in advance to minimize delays.
In order for your application to accommodate hundreds or thousands of users who are simultaneously viewing, modifying, and taking ownership of specific records without suffering from lock timeouts and deadlocks, your database model and query workload will need to be designed for concurrency. SQL-based databases allow you to use normalization techniques to arrive at a data model which not only accurately represents the data you're managing, but also eliminates the risk of duplicate records, double-booking, lost updates, and other anomalies. If your data model is adequately normalized and your application is making proper use of atomic transactions (units of work), the A.C.I.D. properties of SQL-based databases offer powerful, built-in protection for your data with minimal application coding.
I have a Windows application that currently renders graphics largely using MFC that I'd like to change to get better use out of the GPU. Most of the graphics are straightforward and could easily be built up into a scene graph, but some of the graphics could prove very difficult. Specifically, in addition to the normal mesh type objects, I'm also dealing with point clouds which are liable to contain billions of Cartesian stored in a very compact manner that use quite a lot of custom culling techniques to be displayed in real time (Example). What I'm looking for is a mechanism that does the bulk of the scene rendering to a buffer and then gives me access to that buffer, a z buffer, and camera parameters such that I can modify them before putting them out to the display. I'm wondering whether this is possible with Direct3D, OpenGL or possibly use a higher level framework like OpenSceneGraph, and what would be the best starting point? Given the software is Windows based, I'd probably prefer to use Direct3D as this is likely to lead to fewest driver issues which I'm eager to avoid. OpenSceneGraph seems to provide custom culling via octrees, which are close but not identical to what I'm using.
Edit: To clarify a bit more, currently I have the following;
A display list / scene in memory which will typically contain up to a few million triangles, lines, and pieces of text, which I cull in software and output to a bitmap using low performing drawing primitives
A point cloud in memory which may contain billions of points in a highly compressed format (~4.5 bytes per 3d point) which I cull and output to the same bitmap
Cursor information that gets added to the bitmap prior to output
A camera, z-buffer and attribute buffers for navigation and picking purposes
The slow bit is the highlighted part of section 1 which I'd like to replace with GPU rendering of some kind. The solution I envisage is to build a scene for the GPU, render it to a bitmap (with matching z-buffer) based on my current camera parameters and then add my point cloud prior to output.
Alternatively, I could move to a scene based framework that managed the cameras and navigation for me and provide points in view as spheres or splats based on volume and level of detail during the rendering loop. In this scenario I'd also need to be able add cursor information to the view.
In either scenario, the hosting application will be MFC C++ based on VS2017 which would require too much work to change for the purposes of this exercise.
It's hard to say exactly based on your description of a complex problem.
OSG can probably do what you're looking for.
Depending on your timeframe, I'd consider eschewing both OpenGL (OSG) and DirectX in favor of the newer Vulkan 3D API. It's a successor to both D3D and OGL, and is designed by the GPU manufacturers themselves to provide optimal performance exceeding both of its predecessors.
The OSG project is currently developing a Vulkan scenegraph known as VSG, which already demonstrates superior performance to OSG and will have more generalized culling ability.
I've worked a bunch with point clouds and am pretty experienced with them, but I'm not exactly clear on what you're proposing to do.
If you want to actually have a verbal discussion about the matter, I'm pretty easy to find (my company is AlphaPixel -- AlphaPixel.com) and you could call us. I'm in the European time zone right now, it's not clear from your question where you are but you sound US-based.
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I am new to NoSQL world and thinking of replacing my MS Sql Server database to MongoDB. My application (written in .Net C#) interacts with IP Cameras and records meta data for each image coming from Camera, into MS SQL Database. On average, i am inserting about 86400 records per day for each camera and in current database schema I have created separate table for separate Camera images, e.g. Camera_1_Images, Camera_2_Images ... Camera_N_Images. Single image record consists of simple metadata info. like AutoId, FilePath, CreationDate. To add more details to this, my application initiates separate process (.exe) for each camera and each process inserts 1 record per second in relative table in database.
I need suggestions from (MongoDB) experts on following concerns:
to tell if MongoDB is good for holding such data, which eventually will be queried against time ranges (e.g. retrieve all images of a particular camera between a specified hour)? Any suggestions about Document Based schema design for my case?
What should be the specs of server (CPU, RAM, Disk)? any suggestion?
Should i consider Sharding/Replication for this scenario (while considering the performance in writing to synch replica sets)?
Are there any benefits of using multiple databases on same machine, so that one database will hold images of current day for all cameras, and the second one will be used to archive previous day images? I am thinking on this with respect to splitting reads and writes on separate databases. Because all read requests might be served by second database and writes to first one. Will it benefit or not? If yes then any idea to ensure that both databases are synced always.
Any other suggestions are welcomed please.
I am myself a starter on NoSQL databases. So I am answering this at the expense of potential down votes but it will be a great learning experience for me.
Before trying my best to answer your questions I should say that if MS
SQL Server is working well for you then stick with it. You have not
mentioned any valid reason WHY you want to use MongoDB except the fact
that you learnt about it as a document oriented db. Moreover I see
that you have almost the same set of meta-data you are capturing for
each camera i.e. your schema is dynamic.
to tell if MongoDB is good for holding such data, which eventually will be queried against time ranges (e.g. retrieve all images of a particular camera between a specified hour)? Any suggestions about Document Based schema design for my case?
MongoDB being a document oriented db, is good at querying within an aggregate (you call it document). Since you already are storing each camera's data in its own table, in MongoDB you will have a separate collection created for each camera. Here is how you perform date range queries.
What should be the specs of server (CPU, RAM, Disk)? any suggestion?
All NoSQL data bases are built to scale-out on commodity hardware. But by the way you have asked the question, you might be thinking of improving performance by scaling-up. You can start with a reasonable machine and as the load increases, you can keep adding more servers (scaling-out). You no need to plan and buy a high end server.
Should i consider Sharding/Replication for this scenario (while considering the performance in writing to synch replica sets)?
MongoDB locks the entire db for a single write (but yields for other operations) and is meant for systems which have more reads than writes. So this depends upon how your system is. There are multiple ways of sharding and should be domain specific. A generic answer is not possible. However some examples can be given like sharding by geography, by branches etc.
Also read A plain english introduction to CAP Theorem
Updated with answer to the comment on sharding
According to their documentation, You should consider deploying a sharded cluster, if:
your data set approaches or exceeds the storage capacity of a single node in your system.
the size of your system’s active working set will soon exceed the capacity of the maximum amount of RAM for your system.
your system has a large amount of write activity, a single MongoDB instance cannot write data fast enough to meet demand, and all other
approaches have not reduced contention.
So based upon the last point yes. The auto-sharding feature is built to scale writes. In that case, you have a write lock per shard, not per database. But mine is a theoretical answer. I suggest you take consultation from 10gen.com group.
to tell if MongoDB is good for holding such data, which eventually
will be queried against time ranges (e.g. retrieve all images of a
particular camera between a specified hour)?
This quiestion is too subjective for me to answer. From personal experience with numerous SQL solutions (ironically not MS SQL) I would say they are both equally as good, if done right.
Also:
What should be the specs of server (CPU, RAM, Disk)? any suggestion?
Depends on too many variables that only you know, however a small cluster of commodity hardware works quite well. I cannot really give a factual response to this question and it will come down to your testing.
As for a schema I would go for a document of the structure:
{
_id: {},
camera_name: "my awesome camera",
images: [
{
url: "http://I_like_S3_here.amazons3.com/my_image.png" ,
// All your other fields per image
}
]
}
This should be quite easy to mantain and update so long as you are not embedding much deeper since then it could become a bit of pain, however, that depends upon your queries.
Not only that but this should be good for sharding since you have all the data you need in one document, if you were to shard on _id you could probably get the perfect setup here.
Should i consider Sharding/Replication for this scenario (while considering the performance in writing to synch replica sets)?
Possibly, many people assume they need to shard when in reality they just need to be more intelligent in how they design the database. MongoDB is very free form so there are a lot of ways to do it wrong, but that being said, there are also a lot of ways of dong it right. I personally would keep sharding in mind. Replication can be very useful too.
Are there any benefits of using multiple databases on same machine, so that one database will hold images of current day for all cameras, and the second one will be used to archive previous day images?
Even though MongoDBs write lock is on DB level (currently) I would say: No. The right document structure and the right sharding/replication (if needed) should be able to handle this in a single document based collection(s) under a single DB. Not only that but you can direct writes and reads within a cluster to certain servers so as to create a concurrency situation between certain machines in your cluster. I would promote the correct usage of MongoDBs concurrency features over DB separation.
Edit
After reading the question again I omitted from my solution that you are inserting 80k+ images for each camera a day. As such instead of the embedded option I would actually make a row per image in a collection called images and then a camera collection and query the two like you would in SQL.
Sharding the images collection should be just as easy on camera_id.
Also make sure you take you working set into consideration with your server.
to tell if MongoDB is good for holding such data, which eventually
will be queried against time ranges (e.g. retrieve all images of a
particular camera between a specified hour)? Any suggestions about
Document Based schema design for my case?
MongoDB can do this. For better performance, you can set an index on your time field.
What should be the specs of server (CPU, RAM, Disk)? any suggestion?
I think RAM and Disk would be important.
If you don't want to do sharding to scale out, you should consider a larger size of disk so you can store all your data in it.
Your hot data should can fit into your RAM. If not, then you should consider a larger RAM because the performance of MongoDB mainly depends on RAM.
Should i consider Sharding/Replication for this scenario (while
considering the performance in writing to synch replica sets)?
I don't know many cameras do you have, even 1000 inserts/second with total 1000 cameras should still be easy to MongoDB. If you are concerning insert performance, I don't think you need to do sharding(Except the data size are too big that you have to separate them into several machines).
Another problem is the read frequency of your application. It it is very high, then you can consider sharding or replication here.
And you can use (timestamp + camera_id) as your sharding key if your query only on one camera in a time range.
Are there any benefits of using multiple databases on same machine, so
that one database will hold images of current day for all cameras, and
the second one will be used to archive previous day images?
You can separate the table into two collections(archive and current). And set index only on archive if you only query date on archive. Without the overhead of index creation, the current collection should benefit with insert.
And you can write a daily program to dump the current data into archive.
what are ASO and BSO and difference between aggregated storage and block storage?
when to use aggregated and when to use block storage technique.?
Oracle Answers http://docs.oracle.com/cd/E26232_01/doc.11122/esb_dbag/frameset.htm?ainaggr.html
Shortly: If you have а very spare cube and do not need update values in cells by users, use ASO
A very fundamental and frequent question which appears in all Essbase interviews is what is the difference between ASO and BSO applications.
Here are few differences between ASO and BSO
Essbase system has two distinct storage options Aggregate Storage Option (ASO) and Block Storage Option (BSO) each one has its own unique significance.
Characteristics of ASO:
High dimensionality.
No Calculation scripts.
Only one database can be created under one application.
Mandate to fallow the naming conventions for Application name as Applications names should not be metadata, temp, log, default.
Dynamic time series and Time balance properties are not available.
The dimension build process builds any new member then the data will be erased otherwise the data will be alive.
Only one type of partition available (Transparent)
There is no concept of Sparse and Dense dimensions.
No Boolean attribute tag.
Only store data, never share, label only data storage properties are available.
Characteristics of BSO:
Less number of dimensions but shows the business model.
Special functionalities for Accounts and Time dimensions like Dynamic time series, Time balance, Variance reporting.
3 types of partitions Replicated, Transparent, Linked.
Currency conversion is possible.
There is no restriction of the number of databases under one application but performance costs.
Complex calculations can be achieved using calc scripts.
In ASO we can load data at only level 0 where as in BSO we can load data at any level.....
I know BSO a little, so I want to talk about ASO.
ASO(App Store Optimization) is the process of optimizing mobile apps to rank higher in an app store’s search results. The higher your app ranks in an app store’s search results, the more visible it is to potential customers. That increased visibility tends to translate into more traffic to your app’s page in the app store.
The question
Is there a known benchmark or theoretical substantiation on the optimal (rendering speed wise) image size?
A little background
The problem is as follows: I have a collection of very large images, thousands of pixels wide in each dimension. These should be presented to the user and manipulated somehow. In order to improve performance of my web app, I need to slice them. And here is where my question arises: what should be the dimensions of these slices?
You can only find out by testing, every browser will have different performance parameters and your user base may have anything from a mobile phone to a 16-core Xeon desktop. The larger determining factor may actually be the network performance in loading new tiles which is completely dependent upon how you are hosting and who your users are.
As the others already said, you can save a lot of research by duplicating the sizes already used by similar projects: Google Maps, Bing Maps, any other mapping system, not forgetting some of the gigapixel projects like gigapan.
It's hard to give a definitive dimension, but I successfully used 256x256 tiles.
This is also the size used by Microsoft Deep Zoom technology.
In absence of any other suggestions, I'd just use whatever Google Maps is using. I'd imagine they would have done such tests.