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Today, we're going to learn about memory technologies, beginning with SRAM, which has access times of about 0.5 to 2.5 nanoseconds. Can anyone tell me what makes SRAM advantageous for use in computing?
Is it because it's very fast?
Exactly! However, it also comes with a hefty price tag—between $2000 to $5000 per GB. What about DRAM? How does it compare?
DRAM is slower, at around 50 to 70 nanoseconds, but it's much cheaper, right?
Correct! It's significantly more affordable, costing about $20 to $75 per GB. Now, what are the trade-offs we have to consider when selecting memory types?
We need to balance speed and cost. Faster memory is more expensive!
Great point! This balance leads us to memory hierarchy.
Now, who can explain what we mean by memory hierarchy?
It's a structure that organizes different types of memory to optimize speed and capacity!
Exactly! We use faster small memories like cache to augment larger, slower memories like DRAM and hard disks. Why do you think this is beneficial?
Having faster access allows the processor to not wait, increasing overall performance.
Absolutely! This leads to less waiting time, and that brings us to the principle of locality.
Who can remind the class of the principle of locality of reference?
Programs tend to access data in clusters, right?
That's correct! Can anyone differentiate between the two types of locality?
Temporal locality means if we accessed something recently, we will likely access it again, while spatial locality means we access nearby data soon after each other.
Perfect! Now, how does this principle support our memory hierarchy?
We can keep recently accessed data quickly available in cache, based on what we think will be accessed next.
Exactly! It drives how we cache data.
Let's now talk about cache memory. What type of memory is it based on?
SRAM, right?
Right again! It's faster than both DRAM and hard disk. What are some of the metrics that we use to gauge its performance?
Hit time, hit ratio, and miss ratio.
Good job! A higher hit ratio means better performance. Now, can you explain what happens during a cache miss?
When there's a miss, we bring a block of memory from main memory into cache.
Exactly! This action helps take advantage of locality of reference.
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The text outlines the differences between SRAM, DRAM, and magnetic disk storage, emphasizing the trade-offs between speed and cost. It introduces the principle of locality of reference and how it supports memory hierarchy, including the roles of cache memory and main memory in optimizing performance.
In this section, we delve into the different types of memory technologies used in computer systems, notably Static RAM (SRAM), Dynamic RAM (DRAM), and magnetic disks. SRAM boasts access times of 0.5 to 2.5 nanoseconds but comes at a high cost of $2000 to $5000 per GB. In contrast, DRAM is slower at 50 to 70 nanoseconds and cheaper at $20 to $75 per GB. Magnetic disks, while the slowest with access times between 5 to 20 milliseconds, are the most economical at approximately $0.2 to $2 per GB.
To optimize performance, a memory hierarchy is established where faster, smaller, and costlier memories like SRAM and cache supplement larger, cheaper, and slower memories like DRAM and magnetic disks. This hierarchy is deeply influenced by the principle of locality of reference, which states that programs tend to access data in clusters. There are two types of locality: temporal locality, where recently accessed items are likely to be accessed again, and spatial locality, where nearby items are likely to be referenced soon after.
Cache memory, made from SRAM, acts as a bridge between the CPU and main memory to speed up access times, employing hit and miss metrics to gauge efficiency. The section also discusses the calculation of cache lines based on addressable memory words, addressing the necessity of mapping main memory blocks to cache lines. Overall, the section emphasizes that understanding memory hierarchies is crucial for enhancing computer performance.
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Unit 1 part 2: We ended part 1 of unit 1 by saying that we have different memory technologies which vary in terms of their access times and cost per GB. For example, we said that SRAMs are very fast and its speed is about one 0.5 to 2.5 nanoseconds, its access time; that means, it is on an average about one-tenth as fast as the processor ok. However, the cost per GB of this type of memories is also very huge. The cost per GB is about 2000 dollars to 5000 dollars.
Then we have DRAMs which are about 150 to 100 times slower than SRAMs; that means, to bring a certain amount of data a data unit a word from DRAM the processor will require about hundreds of processor cycles to do so. The speed of a DRAM is typically in the range of 50 to 70 nanoseconds; that is the access time is in the range of 50 to 70 nanoseconds. But, it is also about hundred times cheaper than SDRAMs. So, the typical cost of DRAM units range in between 20 dollars to 75 dollars per GB.
This chunk introduces the different types of memory technologies: SRAM and DRAM. SRAM (Static Random Access Memory) is extremely fast, accessing data in 0.5 to 2.5 nanoseconds, but it is also expensive, costing between $2000 to $5000 per GB. In contrast, DRAM (Dynamic Random Access Memory) is much slower at 50 to 70 nanoseconds (150 to 100 times slower than SRAM), but significantly cheaper, costing between $20 to $75 per GB.
Think of SRAM as a high-speed sports car—very fast but also very expensive to maintain. Conversely, DRAM is like a family sedan—slower but more affordable, making it more practical for daily use. Just like how you’d choose the type of car depending on your needs and budget, computer systems choose memory types based on speed and cost requirements.
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Magnetic disks or hard disks are far more cheaper; about 1000 times cheaper than DRAMs being only about 0.2 to 2 dollars per GB. However, it is also about 1000 times 100 times, 100-2000 times slower than DRAM units. Its access times ranges in between 5 to 20 milliseconds. So, to bring a data word from the hard disk, the processor required tens of thousands of processor cycles.
This chunk discusses magnetic disks, which are the most cost-effective storage option at $0.2 to $2 per GB but have slow access times of 5 to 20 milliseconds. This slow performance means that retrieving data from hard disks can require tens of thousands of processor cycles, significantly impacting the overall speed of a computer system.
Imagine trying to fetch a box from the attic (magnetic disk) versus grabbing a snack from the kitchen counter (DRAM). The attic might hold lots of precious items (large storage capacity), but it takes long to get up there and bring something down (slow access time). The kitchen counter is easier and faster, but you might not have as many snacks available at once (less storage space). This illustrates the trade-off between cost, capacity, and speed in memory technologies.
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So, to achieve the best performance what would we desire? We would desire a very large capacity memory which can hold all our programs and data and which works at the pace of the processor. That means, if a processor requires a memory word in one cycle it is available in the processor from memory in the next cycle itself.
This chunk highlights the goal of achieving optimal performance in memory design. Ideally, memory should be large enough to store all needed programs and data while being fast enough to provide the required information to the processor within one cycle. This is essential because delays in fetching instructions or data can slow down processing, impacting overall performance.
Think of this like a chef in a busy restaurant. The chef needs to have all ingredients readily available (large capacity memory) and they need to be able to grab the ingredients quickly (fast access) to keep up with customer orders (processor speed). If the chef has to run into the storeroom every time for missing ingredients, orders will back up, leading to delays just like a processor awaiting data.
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However, in practice we saw the cost and performance parameters and it is difficult to achieve. So, to achieve the greatest performance memory should be able to keep pace with the processor. It is not desirable to wait for instruction slash operands when the processor executes instructions. And hence we would like to use the fastest available memory technology. We also need a large capacity memory to hold all our required information. However, the cost of memory must be reasonable with respect to other components. Hence we understand that we have a design trade off. So, although the faster memories the mem although SRAMs are very fast in terms of access time, they are also very costly. The solution is to have memory hierarchy where smaller more expensive and faster memories are supplemented by larger, cheaper and slower memories.
In this section, the discussion revolves around the inherent challenges of memory design in terms of cost and performance. To achieve high performance, a memory system must maintain a balance between speed and cost. Due to the high cost of the fastest memories, such as SRAM, a memory hierarchy is established, where fast but expensive memory technologies (like SRAM and cache) are complemented by slower and cheaper options (like DRAM and hard disks) to optimize performance.
Imagine running a multi-tiered restaurant where the finest ingredients (fast memory) are very expensive and sourced in small quantities, while bulk ingredients (slower memory) are readily available at lower costs. Chefs use the best ingredients for signature dishes but rely on the bulk supply for common dishes. This mirrors how computers use a memory hierarchy to balance high-speed access with cost and storage capabilities.
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Principle of the locality of reference is based on the fact that programs tend to access data and instructions and data in clusters, in the vicinity in the near vicinity at a of a given memory location. So, programs access a small portion of memory at a given time. Why? Because programs typically contain a large number of loops and subroutines, and within a loop order subroutine a small set of instructions are repeatedly accessed. These instructions again tend to access data in clusters. So, there are two distinct principles in the locality of reference. Temporal locality which says that items access recently are likely to be accessed again. For example, the instructions within a loop. So, if the instructions within in one iteration of the loop will be again accessed in the next iteration of the loop. And special locality in items near those access recently are likely to be accessed soon; for example, sequential access of data from an array.
This chunk explains the principle of locality of reference, which plays a crucial role in optimizing memory design. Programs often access data and instructions located close to each other, resulting in two key types of locality: temporal locality, which refers to the tendency of recently accessed items to be accessed again (like loop instructions), and spatial locality, which asserts that items close to those recently accessed are likely to be accessed soon (like elements in an array). This principle helps design efficient memory management systems.
Consider a person looking for a book in a library. If someone recently checked out a mystery novel, they are likely to return for another mystery book soon (temporal locality). Additionally, these mystery novels are often located on the same shelf (spatial locality), making it quick and easy to find related books. This mirrors how memory access patterns work in computing.
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So, how does this locality how does this principle of the locality of reference helps to maintain this hierarchical memory organization. So, principle of locality makes hierarchical organization of memory possible. So, how can we do that? For example, we can store everything we stored everything in the magnetic disk. And then we copy recently accessed and nearby data in a small DRAM memory or the main memory. So, the main memory uses this technology of DRAM from the disk. So, we have the magnetic disk which stores everything and whatever we require currently we access, we access it and store it in a DRAM or the main memory. Then whatever is still more recently accessed data and instructions are stored in an SRAM memory which is cache from the DRAM.
This chunk articulates how the principle of locality of reference facilitates the formation of a hierarchical memory organization. By storing all data on the slower magnetic disk and transferring recently accessed and nearby data to a faster main memory (using DRAM), and further caching frequently used data to an even faster SRAM memory, it effectively reduces the delay in data access and enhances performance.
Think about how a restaurant might manage stock. All the supplies are kept in a far-off warehouse (magnetic disk), while popular menu items (frequently accessed data) are moved to the kitchen (DRAM). The chef might keep ingredients for today's special (cache) right at their workstation for quick access. This organization improves efficiency and reduces waiting time for customers just as the memory hierarchy speeds up data access for processors.
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Cache memory: So now we begin our discussion on cache memory. So, cache memory as we said is based on the SRAM memory technology. It is a small amount of fast memory which sits between the main memory and the CPU and it may be located within the CPU chip or a separate modules which are plugged in on the motherboard.
This chunk introduces cache memory, which is a smaller and faster type of memory technology based on SRAM. Positioned between the main memory and CPU, cache memory is either integrated within the CPU chip or exists as separate modules on the motherboard. Its purpose is to provide quick access to the most frequently used data and instructions, thereby enhancing overall processing speed.
You can think of cache memory like a chef's immediate workspace where they keep all the most-used tools (cache) ready and accessible, rather than having to fetch them from a store room (main memory). This allows the chef to work more efficiently and quickly, just as cache memory enables the CPU to operate more rapidly.
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Key Concepts
Static RAM (SRAM): Fast but expensive memory used as cache.
Dynamic RAM (DRAM): Slower, cheaper memory used as main memory.
Memory Hierarchy: Improves performance by using faster cache memory with cheaper main memory.
Locality of Reference: Programs tend to access data in close spatial and temporal proximity.
Cache Memory: High-speed storage that optimizes access time for the CPU.
See how the concepts apply in real-world scenarios to understand their practical implications.
An example of SRAM being used as cache in CPUs allows for faster data access than fetching data directly from main memory.
When processing a loop in a program, the CPU continuously accesses the same instructions, demonstrating temporal locality.
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
Cache is fast, cache is small, it helps the CPU to access it all.
Imagine a library where the most borrowed books are kept right by the door. This way, customers can quickly grab the books they frequently read, similar to how cache works.
CATS - Cache, Access time, Transfer speed - remember these when thinking of cache memory!
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Review the Definitions for terms.
Term: SRAM
Definition:
Static Random Access Memory, a type of volatile memory that is faster but more expensive.
Term: DRAM
Definition:
Dynamic Random Access Memory, a type of slower, cheaper volatile memory.
Term: Cache Memory
Definition:
A small-sized type of volatile computer memory that provides high-speed data access to the CPU.
Term: Memory Hierarchy
Definition:
A structure that uses multiple levels of memory storage to optimize performance and cost.
Term: Locality of Reference
Definition:
The principle stating that programs tend to access a set of data or instructions repeatedly in close proximity.
Term: Hit Ratio
Definition:
The fraction of memory accesses that result in a hit.
Term: Miss Ratio
Definition:
The fraction of memory accesses that result in a miss.
Term: Miss Penalty
Definition:
The time penalty incurred when accessing main memory after a cache miss.