CS 439 Principles of Computing Systems focuses on the critical aspects of memory management, which is a vital part of computer system operation, ensuring efficient use of memory resources in various computing contexts.
The storage hierarchy consists of various storage mediums including Tapes, CDs, Disks, Flash/SSD/SCM, Main Memory, and CPU cache levels (L1, L2, L3). Each component plays a distinct role in data access speed, capacity, and cost-effectiveness.
The price per byte significantly decreases as one moves down the hierarchy from L1 cache to tapes, while the speed of memory access similarly trends downward, reflecting a trade-off between cost and performance.
CPU speed has stagnated, yet the sizes of memory storage options continue to expand, highlighting an ongoing need for efficient memory management strategies.
L1 Cache: Access time is approximately 1-2 CPU cycles, providing the fastest data retrieval.
L2 Cache: Slightly slower, taking 10's of CPU cycles.
Main Memory: Typically around 60 nanoseconds, reflecting the increasing access time as one moves from cache to RAM.
Disk Storage: Slower access times, measured in milliseconds, which leads to noticeable delays in data retrieval.
CD ROM: 10's of milliseconds, making it less suitable for rapid access needs.
Tape Storage: Extremely slow, with access times ranging from seconds to minutes, generally used for archiving purposes.
L1 Cache: Ranges from 8-64KB, providing high-speed data access with limited storage capacity.
L2 Cache: Ranges from 256KB to 2MB, balancing speed and size requirements.
L3 Cache: Between 2MB and 32MB, optimized for larger datasets.
Main Memory: Vastly varying sizes from 16MB to 160TB, accommodating diverse application needs.
Disks: Ranging from 1GB to 30TB, used for bulk data storage.
Tapes: Can manage petabytes, suitable for long-term data storage solutions.
Strategies for increasing size while maintaining performance are feasible but require advanced technologies.
Pursuing greater speeds while maintaining memory size is possible but often incurs high costs.
Both goals generally cannot be pursued simultaneously without significant investment or technological breakthroughs.
Memory is conceptualized as a linear array of bytes with addresses ranging from 0 to 2^64 - 1 for a 64-bit address space, facilitating straightforward data access for programmers. Memory does not retain values once a program terminates or when the power is lost, necessitating careful management of data within program lifetimes.
These programs must load and execute at specific memory addresses or ranges, often limiting flexibility in memory usage.
These can be loaded at any address in memory, allowing for more efficient use of memory resources. Linkers prepare relocatable programs, enabling them to execute regardless of their loading positions in memory.
Relocating Linker: Handles address mapping before execution, facilitating versatile program loadability.
Relocating Loader: Resolves addresses at the time of loading into memory, which adds a layer of flexibility but incurs additional overhead during program startup.
Relocation Register: Enables addressing relative to a base register, simplifying how programs reference memory addresses during execution.
Enable effective use of memory for programmers, optimizing both performance and resource consumption.
Protect programs from interfering with each other, enhancing system stability.
Achieve efficient management of physical memory resources, ensuring better system performance.
Historical systems featured basic configurations lacking advanced protection or relocation mechanisms. The structure was simple, involving a single program running alongside a monitor.
Overlaying allows programs larger than available memory to be executed by loading segments or pieces sequentially. Each overlay operates independently, meaning one cannot directly access data from another overlay without explicit loading and management.
Memory is divided into fixed-size partitions, simplifying the loading process but possibly resulting in internal fragmentation and wasted space.
Allocates memory based on the specific requirements of programs, providing greater flexibility. Utilizes relocation registers to support relocatable code, allowing more efficient use of memory.
Refers to memory fragmentation where available free spaces are large but not contiguous, leading to inefficient memory utilization and potential allocation problems.
Involves moving programs in memory to consolidate free spaces, often referred to as "burping," which helps minimize fragmentation and optimizes memory usage.
First Fit: Quickly finds the first sufficient block for allocation, ensuring ease of implementation.
Best Fit: Aims to minimize leftover space post-allocation but can be slower due to the overhead of searching.
Worst Fit: Allocates the largest available block, potentially leading to inefficient space usage.
Buddy Allocation: Divides memory blocks into sizes that are powers of two, making it easier to find suitable allocations.
Enables the execution of multiple programs concurrently, with limits based on available memory resources.
Involves temporarily moving inactive or less critical processes to disk storage, allowing the system to manage memory loads more effectively.
Fragmentation and contiguity issues arise, complicating memory management strategies.
Utilizes a memory management unit (MMU) to translate logical addresses into physical addresses, subdividing addressable space into pages, which are then mapped onto physical frames to enhance access efficiency.
Extends physical memory onto disk space by maintaining only the necessary pages in active memory. Demand paging allows for efficient memory management, preventing the system from being overwhelmed by excessive allocations.
Objects maintain a count of references; deallocation occurs when this count reaches zero, primarily used in languages such as C and C++.
A context-aware method involving marking reachable objects and reclaiming those that are unreachable, aiding in efficient memory utilization.
Effective memory management is crucial for optimizing computing performance and ensuring efficient resource allocation, thereby impacting overall system efficiency and functionality.