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Today, we will learn about hashed page tables. Has anyone heard of them before?
Is it like regular page tables but with some added features?
Exactly! Regular page tables can become very large, especially in systems with extensive address spaces. Hashed page tables help manage that size by only storing entries for valid pages.
How does it limit the number of stored entries?
Great question! When a virtual page number is accessed, a hash function maps it to an index in the table, where we can then find a linked list of entries.
So, are these entries kept in a list for faster access?
Exactly! This reduces the need for a large, single table, enhancing search times and memory efficiency.
In summary, hashed page tables use a hash function to keep track of valid virtual pages efficiently.
Let's explore how hashed page tables function. What happens when we need to access a page?
We run the virtual page number through the hash function?
That's right! This results in an index where we look for the page entries.
What if multiple virtual pages hash to the same index?
In that case, our index will have a linked list. We can traverse this list to find the correct entry.
So that means fewer wasted memory spaces?
Exactly! Since we only store what's necessary, we can handle a larger address space more efficiently. Hashed page tables are particularly useful in 64-bit systems, where address spaces are vast.
To recap, hashed page tables optimize memory usage by employing hash functions for efficient entry access.
Let's summarize the benefits of hashed page tables. Why do you think they're advantageous in large systems?
Less memory wasted means better performance, right?
Precisely! By using only the necessary entries, we minimize waste. What else might be a useful feature?
It speeds up the search process too, since we're not scanning huge tables.
Yes! This broadens the practical usability of memory resources, especially as systems become more complex.
In summary, the advantages of hashed page tables include reducing memory usage and enhancing access speed.
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Hashed page tables use a hash function to map virtual page numbers to entries in a page table, which contains linked lists of elements that can provide details about physical memory. This approach is designed to optimize memory usage and address large address spaces, aiming at efficiency in searching the page entries.
Hashed page tables are designed to tackle the inefficiencies seen in traditional page table approaches, especially as address spaces grow larger. Utilizing hashing techniques, these page tables minimize memory use by storing only necessary entries and allowing for efficient access through a linked list structure. The entry structure is typically organized such that the virtual page number serves as the input to a hash function, allowing a quick lookup in the table. This method is particularly advantageous in systems with extensive virtual address spaces, such as a 64-bit architecture. The hashing technique allows multiple page entries to hash to the same index, forming a linked list that can accommodate collisions without wasting memory. Thus, hashed page tables enable better management of memory resources in modern computing environments.
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The next approach that is used to reduce page table sizes is by using hierarchical page tables. So firstly, what did we use? We used a page table length register. The which was without segmentation and then we said that typically the virtual address space has a stack part and a heap part to address and the page table length register only allows the page table to grow in one direction.
This chunk introduces the reader to the concept of hierarchical page tables and how they help reduce the size of page tables. The discussion begins with the use of a Page Table Length Register (PTLR), which is critical in managing the memory of a virtual address space. The virtual address space is typically divided into sections such as the stack and heap, but a challenge arises because the PTLR allows the page table to grow in only one direction, leading to inefficiencies in memory usage.
Consider your digital wardrobe. If you have a wardrobe with limited shelves, and you can only add clothes from the top, it can become cluttered and disorganized over time. Similarly, without being able to efficiently manage how memory areas (like stack and heap) grow, your program can run into memory allocation issues.
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So, we addressed that by having two segments; one containing possibly the stack, the other containing the heap and each of these two segments has two page tables. And so, by directions two possible directions of increase for the process becomes available.
To solve the limitation of the PTLR, we introduce segmentation, wherein the virtual address space is divided into multiple segments. This allows for separate handling of different data types, such as stack and heap, and provides multiple page tables for each segment. This strategic division means that when one segment grows, the other's size can remain manageable, improving memory efficiency.
Think of this segmentation like having different compartments in a toolbox. If one compartment (the stack) gets full, you can still utilize space in the other compartments (the heap) without compromising the entire toolbox's organization.
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We come to hierarchical page tables. So, here we do not go into segmentation, we don’t have segmentation here and but we have multiple page table levels; hierarchical page tables or multiple page tables multiple multi-level page tables.
Hierarchical page tables are a further refinement of the segmented approach, allowing the logical address space to be broken into multiple levels of page tables. Instead of a single table, a two-level page table system, for example, manages memory more effectively by allowing certain parts of the page tables to occupy physical memory only when needed. This helps to further alleviate memory issues.
Consider a library system where instead of storing all books on a single shelf, books are organized into multiple levels based on genre. This library system ensures efficient organization and access instead of having all books crammed together, making it easy to find a specific one.
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The simplest scheme in this is a two level page table. So, what happens in a two level page table? The page number is split into two parts.
In a two-level page table scheme, the logical address space is divided into two components: one that indexes the outer page table and the other indexes the inner page table. This improved structure means that rather than finding a page in a single table, the system seeks it through two sequential lookups, reducing the memory overhead of maintaining a single large structure.
This is like looking for a book in a multi-sectioned library. Instead of scanning every single book (like a single large page table), you first go to the right section (the outer page table) and then find the specific shelf (the inner page table) in that section.
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Now, two-level paging is not always sufficient. So, even two-levels paging is not sufficient for 64 bit computers. So, what do we do?
While two-level page tables improve memory handling, they can still be inadequate for large address spaces such as those used in 64-bit computing. A two-level scheme can lead to exceedingly large outer page tables, prompting the need for further segmentation to avoid excessive memory usage.
Consider trying to organize a much larger library without additional shelving for new genres. If the library keeps growing but remains with the original two-shelving system, it quickly becomes unmanageable, necessitating more levels to maintain order.
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In order to control in order to control size of another technique that is used to control the size of a page table is by using a hashed page table. It is commonly used for address spaces greater than 32 bits.
Hashed page tables offer a sophisticated solution by allowing pages to be indexed through a hash function, creating a flexible structure that can cater to large virtual address spaces associated with 64-bit systems. This approach limits the search space by linking colliding entries, making data retrieval efficient and minimizing memory usage.
Think of this approach as an online search engine that uses keywords to quickly find relevant documents. Instead of searching through a vast number of records, a quick hash retrieves related documents, making the process faster and requiring less storage for indexing.
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Key Concepts
Hashed Page Tables: A memory management technique that utilizes hashing for efficient virtual page management.
Virtual Page Number: The number assigned to a page that helps in identifying its location.
Hash Function: A method for creating an index in the page table from a virtual page number.
Linked List Structure: Used to manage collisions within the hashed page table for memory efficiency.
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In a 64-bit architecture, using a hashed page table enables the system to effectively manage a vast address space without requiring an immense table for every potential address.
A linked list keeps entries that hash to the same index, allowing for quick lookups while ensuring minimal memory usage.
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Hash it out, don’t shout; if your page is sprout, link it out!
Picture a library of digital pages where each book has more than one title. The librarian uses a unique code (hash function) to point to a shelf, and if two books have the same title, they’re placed in a neat row (linked list) so readers can find them easily.
H.A.S.H. - Hashing Allocates Space for Hash Tables
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Review the Definitions for terms.
Term: Hashed Page Table
Definition:
A method used for memory management that employs hashing to map virtual page numbers to entries for efficient memory access.
Term: Virtual Page Number
Definition:
The address number assigned to a page in a process' virtual address space.
Term: Hash Function
Definition:
A function that converts input data of arbitrary size (like virtual page numbers) into fixed-size values, used for quick indexing.
Term: Linked List
Definition:
A data structure consisting of a sequence of elements, where each element points to the next, allowing for efficient dynamic memory allocation.