ADVANCED COMPUTER ARCHITECTURE (ADC)–Unit 4 - Memory Hierarchy Design Lecture Notes

Anna University

Department of Computer Science Engineering



Subject Code : CS2354

Unit 4

Memory Hierarchy Design

Cache Performance And various cache optimization categories.

The average memory access time is calculated as follows

Average memory access time = hit time + Miss rate x Miss Penalty.

Where Hit Time is the time to deliver a block in the cache to the processor (includes time to determine whether the block is in the cache), Miss Rate is the fraction of memory references not found in cache (misses/references) and Miss Penalty is the additional time required because of a miss

The average memory access time due to cache misses predicts processor performance.

· First, there are other reasons for stalls, such as contention due to I/O devices using memory and due to cache misses· Second, The CPU stalls during misses, and the memory stall time is strongly correlated to average memory access time. CPU time = (CPU execution clock cycles + Memory stall clock cycles) × Clock cycle time

There are 17 cache optimizations into four categories:

1 Reducing the miss penalty: multilevel caches, critical word first, read miss before write miss, merging write buffers, victim caches;

2 Reducing the miss rate larger block size, larger cache size, higher associativity, pseudo-associativity, and compiler optimizations;

3 Reducing the miss penalty or miss rate via parallelism: nonblocking caches, hardware prefetching, and compiler prefetching;

4 Reducing the time to hit in the cache: small and simple caches, avoiding address translation, and pipelined cache access.

various techniques for Reducing Cache Miss Penalty

There are five optimizations techniques to reduce miss penalty.

i) First Miss Penalty Reduction Technique: Multi-Level Caches


The First Miss Penalty Reduction Technique follows the Adding another level of cache between the original cache and memory. The first-level cache can be small enough to match the clock cycle time of the fast CPU and the second-level cache can be large enough to capture many accesses that would go to main memory, thereby the effective miss penalty.

The definition of average memory access time for a two-level cache. Using the subscripts L1 and L2 to refer, respectively, to a first-level and a second-level cache, the formula is Average memory access time = Hit timeL1 + Miss rateL1 × Miss penaltyL1 and Miss penaltyL1 = Hit timeL2 + Miss rateL2 × Miss penaltyL2 so Average memory access time = Hit timeL1 + Miss rateL1× (Hit timeL2 + Miss rateL2 × Miss penaltyL2)

Local miss rate—This rate is simply the number of misses in a cache divided by the total number of memory accesses to this cache. As you would expect, for the first-level cache it is equal to Miss rateL1 and for the second-level cache it is Miss rateL2.

Global miss rate—The number of misses in the cache divided by the total num-ber of memory accesses generated by the CPU. Using the terms above, the global miss rate for the first-level cache is still just Miss rateL1 but for the second-level cache it is Miss rateL1 × Miss rateL2.

This local miss rate is large for second level caches because the first-level cache skims the cream of the memory accesses. This is why the global miss rate is the more useful measure: it indicates what fraction of the memory accesses that leave the CPU go all the way to memory.

Here is a place where the misses per instruction metric shines. Instead of confusion about local or global miss rates, we just expand memory stalls per instruction to add the impact of a second level cache.

Average memory stalls per instruction = Misses per instructionL1× Hit timeL2 + Misses per instructionL2 × Miss penaltyL2.

we can consider the parameters of second-level caches. The foremost difference between the two levels is that the speed of the first-level cache affects the clock rate of the CPU, while the speed of the second-level cache only affects the miss penalty of the first-level cache.

The initial decision is the size of a second-level cache. Since everything in the first- level cache is likely to be in the second-level cache, the second-level cache should be much bigger than the first. If second-level caches are just a little bigger, the local miss rate will be high

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ii) Second Miss Penalty Reduction Technique: Critical Word First and Early


Multilevel caches require extra hardware to reduce miss penalty, but not this second technique. It is based on the observation that the CPU normally needs just one word of the block at a time. This strategy is impatience: Don’t wait for the full block to be loaded before sending the requested word and restarting the CPU. Here are two specific strategies:

Critical word first—Request the missed word first from memory and send it to the CPU as soon as it arrives; let the CPU continue execution while filling the rest of the words in the block. Critical-word-first fetch is also called wrapped fetch and requested word first.

Early restart—Fetch the words in normal order, but as soon as the requested word of the block arrives, send it to the CPU and let the CPU continue execution.

Generally these techniques only benefit designs with large cache blocks, since the benefit is low unless blocks are large. The problem is that given spatial locality, there is more than random chance that the next miss is to the remainder of the block. In such cases, the effective miss penalty is the time from the miss until the second piece arrives.

iii) Third Miss Penalty Reduction Technique: Giving Priority to Read Misses over


This optimization serves reads before writes have been completed. We start with looking at complexities of a write buffer. With a write-through cache the most important improvement is a write buffer of the proper size. Write buffers, however, do complicate memory accesses in that they might hold the updated value of a location needed on a read miss. The simplest way out of this is for the read miss to wait until the write buffer is empty. The alternative is to check the contents of the write buffer on a read miss, and if there are no conflicts and the memory system is available, let the read miss continue. Virtually all desktop and server processors use the latter approach, giving reads priority over writes.

The cost of writes by the processor in a write-back cache can also be reduced. Suppose a read miss will replace a dirty memory block. Instead of writing the dirty block to memory, and then reading memory, we could copy the dirty block to a buffer, then read memory, and then write memory. This way the CPU read, for which the processor is probably waiting, will finish sooner. Similar to the situation above, if a read miss occurs, the processor can either stall until the buffer is empty or check the addresses of the words in the buffer for conflicts.

iv) Fourth Miss Penalty Reduction Technique: Merging Write Buffer

This technique also involves write buffers, this time improving their efficiency. Write through caches rely on write buffers, as all stores must be sent to the next lower level of the hierarchy. As mentioned above, even write back caches use a simple buffer when a block is replaced. If the write buffer is empty, the data and the full address are written in the buffer, and the write is finished from the CPU's perspective; the CPU continues working while the write buffer prepares to write the word to memory. If the buffer contains other modified blocks, the addresses can be checked to see if the address of this new data matches the address of the valid write buffer entry. If so, the new data are combined with that entry, called write merging.

If the buffer is full and there is no address match, the cache (and CPU) must wait until the buffer has an empty entry. This optimization uses the memory more efficiently since multiword writes are usually faster than writes performed one word at a time.



Figure 5.12

The optimization also reduces stalls due to the write buffer being full. Figure 5.12 shows a write buffer with and without write merging. Assume we had four entries in the write buffer, and each entry could hold four 64-bit words. Without this optimization, four stores to sequential addresses would fill the buffer at one word per entry, even though these four words when merged exactly fit within a single entry of the write buffer. Figure 5.12 To illustrate write merging, the write buffer on top does not use it while the write buffer on the bottom does. The four writes are merged into a single buffer entry with write merging; without it, the buffer is full even though three-fourths of each entry is wasted. The buffer has four entries, and each entry holds four 64-bit words. The address for each entry is on the left, with valid bits (V) indicating whether or not the next sequential eight bytes are occupied in this entry. (Without write merging, the words to the right in the upper drawing would only be used for instructions which wrote multiple words at the same time.)

v) Fifth Miss Penalty Reduction Technique: Victim Caches

One approach to lower miss penalty is to remember what was discarded in case it is needed again. Since the discarded data has already been fetched, it can be used again at small cost.

Such “recycling” requires a small, fully associative cache between a cache and its refill path. Figure 5.13 shows the organization. This victim cache contains only blocks that are discarded from a cache because of a miss “victims” and are checked on a miss to see if they have the desired data before going to the next lower-level memory. If it is found there, the victim block and cache block are swapped. The AMD Athlon has a victim cache with eight entries.

Jouppi [1990] found that victim caches of one to five entries are effective at reducing misses, especially for small, direct-mapped data caches. Depending on the program, a four-entry victim cache might remove one quarter of the misses in a 4-KB direct-mapped data cache.


To reduce miss rate

The classical approach to improving cache behavior is to reduce miss rates, and there are five techniques to reduce miss rate. we first start with a model that sorts all misses into three simple categories:

Compulsory—The very first access to a block cannot be in the cache, so the block must be brought into the cache. These are also called cold start misses or first reference misses.

Capacity—If the cache cannot contain all the blocks needed during execution of a program, capacity misses (in addition to compulsory misses) will occur be-cause of blocks being discarded and later retrieved.

Conflict—If the block placement strategy is set associative or direct mapped, conflict misses (in addition to compulsory and capacity misses) will occur be-cause a block may be discarded and later retrieved if too many blocks map to its set. These misses are also called collision misses or interference misses. The idea is that hits in a fully associative cache which become misses in an N-way set associative cache are due to more than N requests on some popular sets.

i ) First Miss Rate Reduction Technique: Larger Block Size

The simplest way to reduce miss rate is to increase the block size. Figure 5.16 shows the trade-off of block size versus miss rate for a set of programs and cache sizes. Larger block sizes will reduce compulsory misses. This reduction occurs because the principle of locality has two components: temporal locality and spatial locality. Larger blocks take advantage of spatial locality.


FIGURE 5.16 Miss rate versus block size for five different-sized caches.

At the same time, larger blocks increase the miss penalty. Since they reduce the number of blocks in the cache, larger blocks may increase conflict misses and even capacity misses if the cache is small. Clearly, there is little reason to increase the block size to such a size that it increases the miss rate. There is also no benefit to reducing miss rate if it increases the average memory access time. The increase in miss penalty may outweigh the decrease in miss rate.

ii) Second Miss Rate Reduction Technique: Larger caches

The obvious way to reduce capacity misses in the above is to increases capacity of the cache. The obvious drawback is longer hit time and higher cost. This technique has been especially popular in off-chip caches: The size of second or third level caches in 2001 equals the size of main memory in desktop computers.

iii) Third Miss Rate Reduction Technique: Higher Associativity:

Generally the miss rates improves with higher associativity. There are two general rules of thumb that can be drawn. The first is that eight-way set associative is for practical purposes as effective in reducing misses for these sized caches as fully associative. You can see the difference by comparing the 8-way entries to the capacity miss, since capacity misses are calculated using fully associative cache. The second observation, called the

2:1 cache rule of thumb and found on the front inside cover, is that a direct-mapped cache of size N has about the same miss rate as a 2-way set-associative cache of size N/2. This held for cache sizes less than 128 KB.

iv) Fourth Miss Rate Reduction Technique: Way Prediction and Pseudo-Associative


In way-prediction, extra bits are kept in the cache to predict the set of the next cache access. This prediction means the multiplexer is set early to select the desired set, and only a single tag comparison is performed that clock cycle. A miss results in checking the other sets for matches in subsequent clock cycles.

The Alpha 21264 uses way prediction in its instruction cache. (Added to each block of the instruction cache is a set predictor bit. The bit is used to select which of the two sets to try on the next cache access. If the predictor is correct, the instruction cache latency is one clock cycle. If not, it tries the other set, changes the set predi ctor, and has a latency of three clock cycles.

In addition to improving performance, way prediction can reduce power for embedded applications. By only supplying power to the half of the tags that are expected to be used, the MIPS R4300 series lowers power consumption with the same benefits.

A related approach is called pseudo-associative or column associative. Accesses proceed just as in the direct-mapped cache for a hit. On a miss, however, before going to the next lower level of the memory hierarchy, a second cache entry is checked to see if it matches there. A simple way is to invert the most significant bit of the index field to find the other block in the “pseudo set.”

Pseudo-associative caches then have one fast and one slow hit time—corresponding to a regular hit and a pseudo hit—in addition to the miss penalty. Figure 5.20 shows the relative times. One danger would be if many fast hit times of the direct-mapped cache became slow hit times in the pseudo-associative cache. The performance would then be degraded by this optimization. Hence, it is important to indicate for each set which block should be the fast hit and which should be the slow one. One way is simply to make the upper one fast and swap the contents of the blocks. Another danger is that the miss penalty may become slightly longer, adding the time to check another cache entry.


v) Fifth Miss Rate Reduction Technique:

Compiler Optimizations

This final technique reduces miss rates without any hardware changes. This magical reduction comes from optimized software. The increasing performance gap between processors and main memory has inspired compiler writers to scrutinize the memory hierarchy to see if compile time optimizations can improve performance. Once again research is split between improvements in instruction misses and improvements in data misses.

Code can easily be rearranged without affecting correctness; for example, reordering the procedures of a program might reduce instruction miss rates by reducing conflict misses. Reordering the instructions reduced misses by 50% for a 2-KB direct-mapped instruction cache with 4-byte blocks, and by 75% in an 8-KB cache.

Another code optimization aims for better efficiency from long cache blocks. Aligning basic blocks so that the entry point is at the beginning of a cache block decreases the chance of a cache miss for sequential code.

Loop Interchange:

Some programs have nested loops that access data in memory in non-sequential order. Simply exchanging the nesting of the loops can make the code access the data in the order it is stored. Assuming the arrays do not fit in cache, this technique reduces misses by improving spatial locality; reordering maximizes use of data in a cache block before it is discarded.

/* Before */

for (j = 0; j < 100; j = j+1) for (i = 0; i < 5000; i = i+1) x[i][j] = 2 * x[i][j];

/* After */

for (i = 0; i < 5000; i = i+1) for (j = 0; j < 100; j = j+1) x[i][j] = 2 * x[i][j];

This optimization improves cache performance without affecting the number of instructions executed.


This optimization tries to reduce misses via improved temporal locality. We are again dealing with multiple arrays, with some arrays accessed by rows and some by columns. Storing the arrays row by row (row major order) or column by column (column major order) does not solve the problem because both rows and columns are used in every iteration of the loop. Such orthogonal accesses mean the transformations such as loop interchange are not helpful.

Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks. The goal is to maximize accesses to the data loaded into the cache before the data are replaced.

virtual memory

Virtual memory divides physical memory into blocks (called page or segment) and allocates them to different processes. With virtual memory, the CPU produces virtual addresses that are translated by a combination of HW and SW to physical addresses, which accesses main memory. The process is called memory mapping or address translation.Today, the two memory-hierarchy levels controlled by virtual memory are DRAMs and magnetic disks

Virtual Memory manages the two levels of the memory hierarchy represented by main memory and secondary storage. Figure 5.31 shows the mapping of virtual memory to physical memory for a program with four pages.


There are further differences between caches and virtual memory beyond those quantitative ones mentioned in Figure 5.32:


Virtual memory also encompasses several related techniques. Virtual memory systems can be categorized into two classes: those with fixed-size blocks, called pages, and those with variable-size locks, called segments. Pages are typically fixed at 4096 to 65,536 bytes, while segment size varies. The largest segment supported on any processor ranges


from 2


bytes up to 2

bytes; the smallest segment is 1 byte. Figure 5.33 shows how the two approaches might divide code and data.


The block can be placed anywhere in main memory. Both paging and segmentation rely on a data structure that is indexed by the page or segment number. This 17 data structure contains the physical address of the block. For segmentation, the offset is added to the segment’s physical address to obtain the final physical address. For paging, the offset is simply concatenated to this physical page address

(see Figure 5.35).clip_image017

This data structure, containing the physical page addresses, usually takes the form of a page table. Indexed by the virtual page number, the size of the table is the number of pages in the virtual address space. Given a 32-bit virtual address, 4-KB pages, and 4 bytes per page table entry, the size of the page table would be formula.

To reduce address translation time, computers use a cache dedicated to these addre ss translations, called a translation look-aside buffer, or simply translation buffer. They are described in more detail shortly.

With the help of Operating System and LRU algorithm pages can be replaced whenever page fault occurs.

Techniques for Fast Address Translation

Page tables are usually so large that they are stored in main memory, and some-times paged themselves. Paging means that every memory access logically takes at least twice as long, with one memory access to obtain the physical address and a second access to get the data. This cost is far too dear.

One remedy is to remember the last translation, so that the mapping process is skipped if the current address refers to the same page as the last one. A more general solution is to again rely on the principle of locality; if the accesses have locality, then the address translations for the accesses must also have locality. By keeping these address translations in a special cache, a memory access rarely re-quires a second access to translate the data. This special address translation cache is referred to as a translation look-aside buffer or TLB, also called a translation buffer or TB.


FIGURE 5.36 Operation of the Alpha 21264 data TLB during address translation.

Selecting a Page Size

The most obvious architectural parameter is the page size. Choosing the page is a question of balancing forces that favor a larger page size versus those favoring a smaller size. The following favor a larger size:

· The size of the page table is inversely proportional to the page size; memory (or other resources used for the memory map) can therefore be saved by making the pages bigger.

· A larger page size can allow larger caches with fast cache hit times.

· Transferring larger pages to or from secondary storage, possibly over a network, is more efficient than transferring smaller pages.

· The number of TLB entries are restricted, so a larger page size means that more memory can be mapped efficiently, thereby reducing the number of TLB misses.

Storage System

Types of Storage Devices

There are various types of Storage devices such as magnetic disks, magnetic tapes, automated tape libraries, CDs, and DVDs.

The First Storage device magnetic disks have dominated nonvolatile storage since 1965. Magnetic disks play two roles in computer systems:

· Long Term, nonvolatile storage for files, even when no programs are running

· A level of the memory hierarchy below main memory used as a backing store for virtual memory during program execution.


A magnetic disk consists of a collection of platters (generally 1 to 12), rotating on a spindle at 3,600 to 15,000 revolutions per minute (RPM). These platters are metal or glass disks covered with magnetic recording material on both sides, so 10 platters have 20 recording surfaces.

The disk surface is divided into concentric circles, designated tracks. There are typically 5,000 to 30,000 tracks on each surface. Each track in turn is divided into sectors that contain the information; a track might have 100 to 500 sectors. A sector is the smallest unit that can be read or written. IBM mainframes allow users to select the size of the sectors, although most systems fix their size, typically at 512 bytes of data. The sequence recorded on the magnetic media is a sector number, a gap, the information for that sector including error correction code, a gap, the sector number of the next sector, and so on.

To read and write information into a sector, a movable arm containing a read/ write head is located over each surface. To read or write a sector, the disk controller sends a command to move the arm over the proper track. This operation is called a seek, and the time to move the arm to the desired track is called seek time.

Average seek time is the subject of considerable misunderstanding. Disk manufacturers report minimum seek time, maximum seek time, and average seek time in their manuals. The first two are easy to measure, but the average was open to wide interpretation.

The time for the requested sector to rotate under the head is the rotation latency or rotational delay. The average latency to the desired information is obviously halfway around the disk; if a disk rotates at 10,000 revolutions per minute (RPM), the average rotation time is therefore

Average Rotation Time = 0.5/10,000RPM = 0.5/(10,000/60)RPM = 3.0ms

The next component of disk access, transfer time, is the time it takes to transfer a block of bits, typically a sector, under the read-write head. This time is a function of the block size, disk size, rotation speed, recording density of the track, and speed of the electronics connecting the disk to computer. Transfer rates in 2001 range from 3 MB per second for the 3600 RPM, 1-inch drives to 65 MB per second for the 15000 RPM, 3.5- inch drives.

The Future of Magnetic Disks

The disk industry has concentrated on improving the capacity of disks. Improvement in capacity is customarily expressed as improvement in areal density, measured in bits per square inch:

Areal Density = (Tracks/Inch) on a disk surface X (Bits/Inch) on a track

Through about 1988 the rate of improvement of areal density was 29% per year, thus doubling density every three years. Between then and about 1996, the rate improved to

60% per year, quadrupling density every three years and matching the traditional rate of DRAMs. From 1997 to 2001 the rate increased to 100%, or doubling every year. In 2001, the highest density in commercial products is 20 billion bits per square inch, and the lab record is 60 billion bits per square inch.

Optical Disks:

One challenger to magnetic disks is optical compact disks, or CDs, and its successor, called Digital Video Discs and then Digital Versatile Discs or just DVDs. Both the CD-ROM and DVD-ROM are removable and inexpensive to manufacture, but they are read-only mediums. These 4.7-inch diameter disks hold 0.65 and 4.7 GB, respectively, although some DVDs write on both sides to double their capacity. Their high capacity and low cost have led to CD-ROMs and DVD-ROMs replacing floppy disks as the favorite medium for distributing software and other types of computer data.

The popularity of CDs and music that can be downloaded from the WWW led to a market for rewritable CDs, conveniently called CD-RW, and write once CDs, called CD-R. In 2001, there is a small cost premium for drives that can record on CD-RW. The media itself costs about $0.20 per CD-R disk or $0.60 per CD-RW disk. CD-RWs and CD-Rs read at about half the speed of CD-ROMs and CD-RWs and CD-Rs write at about a quarter the speed of CD-ROMs.

Magnetic Tape:

Magnetic tapes have been part of computer systems as long as disks because they use the similar technology as disks, and hence historically have followed the same density improvements. The inherent cost/performance difference between disks and tapes i s based on their geometries:

· Fixed rotating platters offer random access in milliseconds, but disks have a limited storage area and the storage medium is sealed within each reader.

· Long strips wound on removable spools of “unlimited” length mean many tapes can be used per reader, but tapes require sequential access that can take seconds.

One of the limits of tapes had been the speed at which the tapes can spin without breaking or jamming. A technology called helical scan tapes solves this problem by keeping the tape speed the same but recording the information on a diagonal to the tape with a tape reader that spins much faster than the tape is moving. This technology increases recording density by about a factor of 20 to 50. Helical scan tapes were developed for low-cost VCRs and camcorders, which brought down the cost of the tapes and readers.

Automated Tape Libraries

Tape capacities are enhanced by inexpensive robots to automatically load and store tapes, offering a new level of storage hierarchy. These nearline tapes mean access to terabytes of information in tens of seconds, without the intervention of a human operator.

Flash Memory

Embedded devices also need nonvolatile storage, but premiums placed on space and power normally lead to the use of Flash memory instead of magnetic recording. Flash memory is also used as a rewritable ROM in embedded systems, typically to allow software to be upgraded without having to replace chips. Applications are typically prohibited from writing to Flash memory in such circumstances.

Like electrically erasable and programmable read-only memories (EEPROM), Flash memory is written by inducing the tunneling of charge from transistor gain to a floating gate. The floating gate acts as a potential well which stores the ch arge, and the charge cannot move from there without applying an external force. The primary difference between EEPROM and Flash memory is that Flash restricts write to multi - kilobyte blocks, increasing memory capacity per chip by reducing area dedicated to control. Compared to disks, Flash memories offer low power consumption (less than 50 milliwatts), can be sold in small sizes, and offer read access times comparable to DRAMs. In 2001, a 16 Mbit Flash memory has a 65 ns access time, and a 128 Mbit Flash memory has a 150 ns access time.

1. Buses : Connecting I/O Devices to CPU/Memory

Buses were traditionally classified as CPU-memory buses or I/O buses. I/O buses may be lengthy, may have many types of devices connected to them, have a wide range in the data bandwidth of the devices connected to them, and normally follow a bus standard. CPU-memory buses, on the other hand, are short, generally high speed, and matched to the memory system to maximize memory-CPU bandwidth. During the design phase, the designer of a CPU-memory bus knows all the types of devices that must connect together, while the I/O bus designer must accept devices varying in latency and bandwidth capabilities. To lower costs, some computers have a single bus for both memory and I/O devices. In the quest for higher I/O performance, some buses are a hybrid of the two. For example, PCI is relatively short, and is used to connect to more traditional I/O buses via bridges that speak both PCI on one end and the I/O bus protocol on the other. To indicate its intermediate state, such buses are sometimes called mezzanine

Bus Design Decisions

The design of a bus presents several options, as Figure 7.8 shows. Like the rest of the computer system, decisions depend on cost and performance goals. The first t hree options in the figure are clear—separate address and data lines, wider data lines, and multiple-word transfers all give higher performance at more cost.


The next item in the table concerns the number of bus masters. These devices can initiate a read or write transaction; the CPU, for instance, is always a bus master. A bus has multiple masters when there are multiple CPUs or when I/O devices can initiate a bus transaction. With multiple masters, a bus can offer higher bandwidth by using packets, as opposed to holding the bus for the full transaction. This technique is called split transactions.

The final item in Figure 7.8, clocking, concerns whether a bus is synchronous or asynchronous. If a bus is synchronous, it includes a clock in the control lines and a fixed protocol for sending address and data relative to the clock. Since little or no logic is needed to decide what to do next, these buses can be both fast and inexpensive.

Bus Standards

Standards that let the computer designer and I/O-device designer work independently play a large role in buses. As long as both designers meet the requirements, any I/O device can connect to any computer. The I/O bus standard is the document that defines how to connect devices to computers.

• The Good

– Let the computer and I/O-device designers work independently

– Provides a path for second party (e.g. cheaper) competition

• The Bad

– Become major performance anchors

– Inhibit change

• How to create a standard

– Bottom-up

• Company tries to get standards committee to approve it’s latest philosophy in hopes that they’ll get the jump on the others (e.g. S bus, PC-AT bus, ...)

• De facto standards

– Top-down

• Design by committee (PCI, SCSI, ...) Some sample bus designs are shown below


Interfacing Storage Devices to the CPU

The I/O bus is connected to the main memory bus is shown in figure 7.15clip_image021

Processor interface with i/o bus can be done with two techniques one using interrupts and second using memory mapped I/O

• I/O Control Structures

– Polling

– Interrupts


– I/O Controllers

– I/O Processors

The simple interface, in which the CPU periodically checks status bits to see if it is time for the next I/O operation, is called polling.

Interrupt-driven I/O, used by most systems for at least some devices, allows the CPU to work on some other process while waiting for the I/O device. For example, the LP11 has a mode that allows it to interrupt the CPU whenever the done bit or error bit is set. In general-purpose applications, interrupt-driven I/O is the key to multitasking operating systems and good response times.

The drawback to interrupts is the operating system overhead on each event. In real-time applications with hundreds of I/O events per second, this overhead can be intolerable. One hybrid solution for real-time systems is to use a clock to periodically interrupt the CPU, at which time the CPU polls all I/O devices

The DMA hardware is a specialized processor that transfers data between memory and an I/O device while the CPU goes on with other tasks. Thus, it is external to the CPU and must act as a master on the bus. The CPU first sets up the DMA registers, which contain a memory address and number of bytes to be transferred. More sophisticated DMA devices support scatter/gather, whereby a DMA device can write or read data from a list of separate addresses. Once the DMA transfer is complete, the DMA controller interrupts the CPU. There may be multiple DMA devices in a computer system.

2.RAID : Redundant Arrays of Inexpensive Disks

An innovation that improves both dependability and performance of storage systems is disk arrays. One argument for arrays is that potential throughput can be increased by having many disk drives and, hence, many disk arms, rather than one large drive with one disk arm. Although a disk array would have more faults than a smaller number of larger disks when each disk has the same reliability, dependability can be improved by adding redundant disks to the array to tolerate faults. That is, if a single disk fails, the lost information can be reconstructed from redundant information. The only danger is in having another disk fail between the time the first disk fails and the time it is replaced (termed mean time to repair, or MTTR). Since the mean time to failure (MTTF) of disks is tens of years, and the MTTR is measured in hours, redundancy can make the measured reliability of 100 disks much higher than that of a single disk. These systems have become known by the acronym RAID, stand-ing originally for redundant array of inexpensive disks, although some have re-named it to redundant array of independent disks

The several approaches to redundancy have different overhead and perfor-mance. Figure 7.17 shows the standard RAID levels. It shows how eight disks of user data must be supplemented by redundant or check disks at each RAID level. It also shows the minimum number of disk failures that a system would survive.



FIGURE 7.17 RAID levels, their fault tolerance, and their overhead in redundant disks.

No Redundancy (RAID 0)

This notation is refers to a disk array in which data is striped but there is no redundancy to tolerate disk failure. Striping across a set of disks makes the collection appear to software as a single large disk, which simplifies storage management. It also improves performance for large accesses, since many disks can operate at once. Video editing systems, for example, often stripe their data.

RAID 0 something of a misnomer as there is no redundancy, it is not in the original RAID taxonomy, and striping predates RAID. However, RAID levels are often left to the operator to set when creating a storage system, and RAID 0 is often listed as one of the options. Hence, the term RAID 0 has become widely used.

Mirroring (RAID 1)

This traditional scheme for tolerating disk failure, called mirroring or shadowing, uses twice as many disks as does RAID 0. Whenever data is written to one disk, that data is also written to a redundant disk, so that there are always two copies of the information. If a disk fails, the system just goes to the “mirror” to get the desired information.

Mirroring is the most expensive RAID solution, since it requires the most disks.

The RAID terminology has evolved to call the former RAID 1+0 or RAID 10 (“striped mirrors”) and the latter RAID 0+1 or RAID 01 (“mirrored stripes”).

Bit-Interleaved Parity (RAID 3)

The cost of higher availability can be reduced to 1/N, where N is the number of disks in a protection group. Rather than have a complete copy of the original data for each disk, we need only add enough redundant information to restore the lost information on a failure. Reads or writes go to all disks in the group, with one extra disk to hold the check information in case there is a failure. RAID 3 is popular in applications with large data sets, such as multimedia and some scientific codes.

Parity is one such scheme. Readers unfamiliar with parity can think of the redundant disk as having the sum of all the data in the other disks. When a disk fails, then you subtract all the data in the good disks from the parity disk; the remaining information must be the missing information. Parity is simply the sum modulo two. The assumption behind this technique is that failures are so rare that taking longer to recover from failure but reducing redundant storage is a good trade-off.

Block-Interleaved Parity and Distributed Block-Interleaved Parity (RAID 4 and RAID 5) In RAID 3, every access went to all disks. Some applications would prefer to do smaller accesses, allowing independent accesses to occur in parallel. That is the purpos e of the next RAID levels. Since error-detection information in each sector is checked on reads to see if data is correct, such “small reads” to each disk can occur independently as long as the minimum access is one sector.

Writes are another matter. It would seem that each small write would demand that all other disks be accessed to read the rest of the information needed to recalculate the new parity, as in Figure 7.18. A “small write” would require reading the old data and old parity, adding the new information, and then writing the new parity to the parity disk and the new data to the data disk.


RAID 4 efficiently supports a mixture of large reads, large writes, small reads, and small writes. One drawback to the system is that the parity disk must be updated on every write, so it is the bottleneck for back-to-back writes. To fix the parity-write bottleneck, the parity information can be spread throughout all the disks so that there is no single bottleneck for writes. The distributed parity organization is RAID 5.



Figure 7.19 shows how data are distributed in RAID 4 vs. RAID 5. As the organization on the right shows, in RAID 5 the parity associated with each row of data blocks is no longer restricted to a single disk. This organization allows multiple writes to occur simultaneously as long as the stripe units are not located in the same disks. For example, a write to block 8 on the right must also access its parity block P2, thereby occupying the first and third disks. A second write to block 5 on the right, implying an update to its parity block P1, accesses the second and fourth disks and thus could occur at the same time as the write to block 8. Those same writes to the organization on the left would result in changes to blocks P1 and P2, both on the fifth disk, which would be a bottleneck.

P+Q redundancy (RAID 6)

Parity based schemes protect against a single, self-identifying failures. When a single failure is not sufficient, parity can be generalized to have a second calculation over the data and another check disk of information. Yet another parity block is added to allow recovery from a second failure. Thus, the storage overhead is twice that of RAID 5. The small write shortcut of Figure 7.18 works as well, ex-cept now there are six disk accesses instead of four to update both P and Q information.

Errors and Failures in Real Systems

Publications of real error rates are rare for two reasons. First academics rarely have access to significant hardware resources to measure. Second industrial, researchers are rarely allowed to publish failure information for fear that it would be used against their companies in the marketplace. Below are four exceptions.

Berkeley’s Tertiary Disk

The Tertiary Disk project at the University of California created an art-image server for the Fine Arts Museums of San Francisco. This database consists of high quality images of over 70,000 art works. The database was stored on a clus-ter, which consisted of 20 PCs containing 368 disks connected by a switched Ethernet. It occupied in seven 7 - foot high racks.

Total in Total % Component

System Failed Failed

SCSI Controller 44 1 2.3%

SCSI Cable 39 1 2.6%

SCSI Disk 368 7 1.9%

IDE Disk 24 6 25.0%

Disk Enclosure -

46 13 28.3% Backplane

Disk Enclosure - Power

92 3 3.3% Supply

Ethernet Controller 20 1 5.0%

Ethernet Switch 2 1 50.0%

Ethernet Cable 42 1 2.3%

CPU/Motherboard 20 0 0%

FIGURE 7.20 Failures of components in Tertiary Disk over eighteen months of oper- ation.

Figure 7.20 shows the failure rates of the various components of Tertiary Disk. In advance of building the system, the designers assumed that data disks would be the least reliable part of the system, as they are both mechanical and plentiful. As Tertiary Disk was a large system with many redundant components, it had the potential to survive this wide range of failures. Components were connected and mirrored images were placed no single failure could make any image unavailable. This strategy, which initially appeared to be overkill, proved to be vital.

This experience also demonstrated the difference between transient faults and hard faults. Transient faults are faults that come and go, at least temporarily fixing themselves. Hard faults stop the device from working properly, and will continue to misbehave until repaired.


The next example comes from industry. Gray [1990] collected data on faults for Tandem Computers, which was one of the pioneering companies in fault tolerant computing. Figure 7.21 graphs the faults that caused system failures between 1985 and 1989 in absolute faults per system and in percentage of faults encoun-tered. The data shows a clear improvement in the reliability of hardware and maintenance. Disks in 1985 needed yearly service by Tandem, but they were re-placed by disks that needed no scheduled maintenance. Shrinking number of chips and connectors per system plus software’s ability to tolerate hardware faults reduced hardware’s contribution to only 7% of failures by 1989. And when hardware was at fault, software embedded in the hardware device (firmware) was often the culprit. The data indicates that software in 1989 was the major source of reported outages (62%), followed by system operations (15%).


1985                                           1987                                     1989



The problem with any such statistics are that these data only refer to what is reported; for example, environmental failures due to power outages were not reported to Tandem because they were seen as a local problem.


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