Performance Comparison of IDE and SCSI Disks
Brian White Wee Teck Ng, Bruce K. Hillyer
University of Virginia Bell Laboratories, Lucent Technologies
bsw9d@cs.virginia.edu {weeteck, bruce}@research.bell-labs.com
It is widely believed that the IDE disks found in PCs are inexpensive but slow, whereas the SCSI disks used
in servers and workstations are faster, more reliable, and more manageable. The belief that current IDE disks
have performance and reliability disadvantages has been called into question by several recent reports. Thus
we consider the possibility of achieving tremendous cost advantages by using IDE disks as the foundation of
a storage system.
In this paper, we give an extensive performance comparison of IDE and SCSI disks. We measure their
performance on a variety of micro benchmarks and macro benchmarks, and we explain these results with the
help of kernel instrumentation and device activity traces collected by a SCSI analyzer. We consider the
impact of several factors, including sequential vs. random workloads, file system enhancements such as
journaling and Soft Updates, I/O scheduling in the kernel vs. in the disk drive (as enabled by tagged
queuing), and the use of RAID technology to obtain I/O parallelism. In our testbed we find that the IDE disk
is faster than the SCSI disk for sequential I/O, but the SCSI disk is faster for random I/O. We also observe
that the random I/O performance deficit of the IDE disk is partly overcome by kernel I/O scheduling, and is
further mitigated by scheduling in the drive (as enabled by tagged queuing), and by the use of journaling and
Soft Updates. Taken as a whole, our results lead us to conclude that RAID systems based on IDE drives can
be both faster and significantly less expensive than SCSI RAID systems.
1 Introduction
In this paper, we focus on a performance comparison of two popular hard disks types, IDE and SCSI.
IDE (Integrated Device Electronics) disks, also referred to in the industry as ATA (AT attachment) or EIDE
(Enhanced IDE), are the dominant storage for home computers and personal workstations, and have 85%
share of the disk drive market [25]. By contrast, SCSI (Small Computer System Interconnect) disks dominate
the workstation and server markets. IDE disks are much less expensive than SCSI disks: Chung et al. [24]
note the potential to store a terabyte using IDE disks for less than $10,000. To capitalize on this cost
advantage, companies such as Adaptec, Promise, Raidzone, Raidweb, Jetstor, and disk manufacturers Maxtor
and Quantum have introduced RAID IDE controllers [1, 11, 23] and IDE RAID systems.
The desire to provide more cost-effective storage solutions, on various scales, motivates a thorough
comparison of the IDE and SCSI technologies. We distinguish our work in this area from previous efforts
(see Section 5) by combining the following properties in this study. Our experimental testbed is specified in
detail, the device specifications of our IDE and SCSI configurations are very similar, our performance
experiments are comprehensive and clearly defined, the experiments represent a wide-range of configurations
and workloads, and the analysis of benchmark timing measurements is supported by kernel instrumentation
and by physical I/O traces obtained via a SCSI analyzer. In addition to experimental analysis, we examine
four factors that partly compensate for the native performance limitations of IDE, namely disk concurrency,
disk scheduling, file system design, and I/O parallelism.
This paper is organized as follows. Section 2 describes differences between IDE and SCSI disks in terms
of price, performance-related specifications, and interface-related specifications. Section 3 describes our
experimental testbed and the benchmarks that we use for timing measurements. Section 4 presents the
benchmark results and analysis. Section 5 covers related work, and in Section 6 we provide concluding
2 IDE versus SCSI
In this section, we compare IDE and SCSI technology in terms of price, performance specifications, and
interface specifications, because these considerations are important in assessing the relative utility of these
two technology families. To give representative device characteristics, we display specifications for six
current disks and controllers in Table 1. In Table 2 we show several interface characteristics that
differentiate between IDE and SCSI devices (see [25] for a more detailed comparison of these interfaces).
Model Rotational
Capacity Avg. Seek
Buffer Size Cost Cost/
IBM 75GXP 7200 RPM 30 GB 7 ms 37 MB/s 2 MB $159 $5.30
Seagate Barracuda ATA II 7200 RPM 30 GB 8.9 ms 30 MB/s 2 MB $165 IDE Disks $5.50
Maxtor DM Plus40 7200 RPM 30 GB 8.7 ms 49.5 MB/s 2 MB $155 $5.17
IBM Ultrastar 36LZX 10000 RPM 36 GB 4.9/5.9 ms 22-36 MB/s 4 MB $550 $15.11
Seagate Cheetah 36LP 10000 RPM 36 GB 5.2/6.0 ms 38.5 MB/s 4 MB $633 $17.39
Quantum Atlas 10K II 10000 RPM 36 GB 5.5 ms 18-26 MB/s 8 MB $815 $22.39
Model RAID
Cache Cost Cost/
Promise Ultra100 None 4 200 MB/s None $25 $6.25
Abit Hotrod100 0, 1, 0/1 4 200 MB/s None $45 $11.25
3ware 6800 0, 1, 1/0 8 528 MB/s 84-100 MB/s None $500 $62.50
Adaptec 2940U2W None 15 160 MB/s None $250 $16.67
Adaptec 2200S 0, 1, 0/1, 5 15 160 MB/s 32 $499 $33.27
Adaptec 3200S 0, 1, 0/1, 5 30 320 MB/s 32 $825 $27.50
Table 1: Disk and Controller Price Comparison. Price figures are from www.dirtcheapdrives.com and
www.buy.com 22 November 2000.
From Table 1 we see that the price differential between SCSI and IDE is enormous. The lower price of
IDE devices reflects ferocious competition and economies of scale. On balance, the IDE disks in the table
have higher sustained bandwidths, but we see faster seek times and rotational speeds, and larger buffer sizes
for SCSI disks. These differences largely reflect market forces rather than distinctions inherent to the
Structure Interface to disks, CD-ROM Bus supporting many types of devices
Controller On-board integrated controller SCSI host bus adapter
Width 16-bit 8-bit, 16-bit, 32-bit
Bus Bandwidth 3.3-100 MB/sec 5-160 MB/sec
100 MB/sec
150 MB/sec
300 MB/sec
600 MB/sec
160 MB/sec
320 MB/sec
640 MB/sec
Cable Length 46 cm (18 in)
Supports only internal devices
1.5-12 meters (12 meters with
differential SCSI). Requires termination
Number of Devices 2 per channel 15 per channel (wide SCSI)
Data Transfer Modes PIO or DMA DMA
Command Concurrency Immature Tagged Queuing
(Max 32 tags)
Tagged Queuing
(Max 256 tags)
Bus Concurrency Limited disconnect/reconnect Disconnect/reconnect allows concurrent
bus access
Manageability S.M.A.R.T. S.M.A.R.T., SCAM
Table 2: Overview of IDE and SCSI Interface. The IDE features are from the ATA/ATAPI-5 standards
(T13/T1321D Rev 2). The SCSI features are from the SCSI-2 and SCSI-3 standards (ANSI X3.131,
NCITS.306, x3.270, x3.292, x3.301). Some features (e.g. S.M.A.R.T) are specific to the disk drive vendors
and are not part of the standards [6, 35]. The future bus speed projections are from [5, 10].
From Table 2 we see several characteristics in the SCSI specifications that could lead to higher
performance. SCSI has higher bus frequencies, wider bus widths, greater I/O concurrency (up to 256
outstanding requests), and more extensive disconnect/reconnect handling to reduce bus idle time. In
addition, SCSI supports independent transfer rates for a mixture of slow and fast devices, whereas IDE runs
all devices on the bus at the "least common denominator" i.e., the slowest bandwidth and data transfer mode
of any device connected to the bus.
The performance experiments in this paper show that factors such as disk concurrency, disk scheduling,
file system design, and I/O parallelism can cause the observed system performance to differ significantly
from what is suggested by a cursory examination of the disk drive specifications. For example, on a software
development benchmark (SDET), the performance difference between IDE and SCSI disks is 22% using the
default Unix Fast File System (FFS), but only 1% using the Soft Update enhancement to FFS.
SCSI systems generally are considered to be more robust, manageable, and scalable than IDE systems,
but many of the perceived reliability problems of IDE systems stem from early implementations that had
defects in chipsets and device drivers [7], or are the consequence of improper cooling and operation [34].
Current IDE and SCSI disks should have similar reliability since they share common technology and
components [1]. Moreover, storage vendors that sell both IDE and SCSI RAID systems (and thus have no
vested interest in either product) note no major differences in disk failure rates between the two types of
disks within the disk warranty period [16]. A SCSI disk typically has a 5 year warranty versus 3 years for an
IDE disk, but rapid advances in disk technology render disks obsolescent before the expiration of the
warranty period [22]. In terms of manageability, SCSI provides a richer command set that enables a user to
query and configure device characteristics, and to control recovery from errors. Moreover, the SCSI physical
interface supports a larger number of devices per bus and longer cables. RAID boxes that are constructed
from IDE devices can obtain many of the advantages of the SCSI interface by exporting a SCSI interface to
the host.
3 Measurement Methodology
This section describes our testbed for performance measurements, and the benchmark programs that we
use to generate storage workloads.
3.1 Testbed
Our platform is a Dell OptiPlex PC equipped with an Intel D820LP motherboard, an Intel 1 GHz
Pentium III processor, 512 MB RDRAM, and a 30 GB IDE system disk connected to the on-board IDE
controller. For the IDE experiments, we use IBM Deskstar 75GXP disks, an Abit HotRod IDE controller
with HPT 370 chipset, and a Raidweb (www.raidweb.com) IDE RAID array. For the SCSI experiments we
use IBM Ultrastar 36ZX disks, an Adaptec 29160 host bus adapter (HBA), and a TSR-2200
(www.terasolutions.com) SCSI RAID array. Table 3 shows the detailed disk specifications. The IDE and
SCSI disk settings are mostly comparable: we enable the read and write cache on both IDE and SCSI disks,
and both disks support readahead. We enable disconnects and queue management (DQue and Queue
Algorithm Modifier) [35] on the SCSI disk, but we are unable to ensure similar settings on the IDE disk as
these parameters are not exposed by the IDE interface. The SCSI and IDE RAID arrays are comparable: 128
MB of SDRAM, 8 disks (IBM Deskstar75GXP or IBM Ultrastar36ZX) configured as RAID 5, and an Intel
i960 processor. IBM disks are advantageous for experimentation because of detailed documentation for
configuration parameters (e.g. Queue Algorithm Modifier) and internal measurement features (e.g. cache
statistics). To confirm the generality of our findings, we repeat selected tests on Seagate ST318203LW and
Maxtor DiamondMax Plus 40 disks, and on Tekram DC390U2W and Promise Ultra100 host adapters. We
use a Verisys SV8160 SCSI bus analyzer (www.verisys.com) to capture I/O traces from the SCSI bus.
The operating systems in our experiments are FreeBSD 5.0–20001108 and Windows NT 4.0 build 1381.
To control disk concurrency (i.e., enable tag queuing and set number of tags), in FreeBSD we use the
camcontrol command, and in Windows NT we use the Registry Editor. In FreeBSD experiments we control
disk scheduling by marking requests with the BIO_ORDERED attribute to restrict request reordering. We
cannot modify the disk scheduler behavior in Windows NT (we do not have the source code).
Model IBM Deskstar 75GXP IBM Ultrastar36ZX
Capacity 30 GB 18 GB
RPM 7200 10000
Average Seek Time (read/write) 7.0 ms/8.0 ms 4.9 ms/5.9 ms
Interface ATA-100 SCSI Ultra2 (Wide) LVD
Interface Bandwidth 100 MB/sec 80 MB/sec
Media Sustained 37.0 MB/sec 29.5 MB/sec
Predictive Failure Analysis S.M.A.R.T. compliant S.M.A.R.T. compliant
Buffer Size 2 MB 2MB
Table 3: Disk Specifications. All parameters are extracted from the respective disk drive manuals [6, 35].
3.2 Benchmarks
We choose our benchmarks to represent general-purpose computing and internet service environments.
The goal of our evaluation is twofold. First, we seek to understand how IDE and SCSI perform under
“realistic” I/O-intensive workloads. Second, we want to understand the impact of file system and storage
system parameters on IDE and SCSI performance.
We use two types of benchmarks. Our micro benchmarks quantify the performance of basic I/O
operations like reads and writes. Our macro benchmarks measure general purpose programming workloads
(SSH and SPEC SDET) and internet workloads (PostMark and NetNews). We run all macro benchmarks on
a newly created file system to eliminate the effects of file system aging [29]. For the micro benchmarks, we
conduct 10 experiments for each data point. Due to the duration of the macro benchmarks, we were only
able to run 2-3 iterations of each experiment, but we observed consistent results across these runs. We
computed standard deviations and investigated or re-tested anomalies. We verified that the durations of the
macro benchmarks and sizes of the micro benchmarks are sufficient to prevent hitting in the buffer cache and
disk cache across experiments.
We begin with a set of micro benchmarks that read or write 10 MB from the disk under test, through
the raw disk device to avoid the impact of the file system buffer cache [19]. The test forks off 128 children
that each issue an 8KB I/O request, resulting in 128 outstanding I/O requests. We use semaphores and shared
memory to coordinate the I/Os, and we verify that all 128 children are active concurrently via
instrumentation and SCSI bus analysis. The sequential program accesses data stored sequentially. The
random program accesses data stored at random addresses. The interleave test accesses data sequentially
from two disk locations: one sequential stream starts at block zero, another sequential stream starts at the
middle of the disk. The I/O requests from these two sequential streams are interleaved. This access pattern is
common in some file systems like Microsoft Windows NTFS, which maintains log files at two disk locations
(i.e. MFT and MFTmirror [30]).
The SSH benchmark is proposed by Seltzer [27] as a replacement for the popular Andrew File System
benchmark. It unpacks, configures, and builds a medium-sized software package (SSH). The unpack phase
extracts a compressed tar archive containing the SSH source tree. It thus reads a large file sequentially and
generates many subsequent small metadata operations. The config phase determines what features are
available on the host operating system and generates makefiles. To do this, it compiles and executes many
small test programs, causing many file system metadata operations. The build phase compiles and links the
ssh executable. We run the three phases of the benchmark consecutively, consequently the config and build
phases run with the file system cache warmed by the previous phases.
The SPEC SDET benchmark [8] is designed to emulate a typical timesharing workload. It models a
software development environment (e.g., editing, compilation, and various UNIX utilities) on a time-sharing
host, and makes fairly extensive use of the file system. Although this benchmark models a time-sharing
environment rather than today's client-server environment [27], it is nonetheless useful because it has tunable
concurrency, and its basic operations are still representative of the software development workload.
The PostMark benchmark is designed to model the workload seen by Internet Service Providers under
heavy load [13]. It is meant to model a combination of electronic mail, netnews, and web-based commerce
transactions. It creates a large set of files with random sizes within a set range. The files are then subjected to
a number of transactions like file create, delete, read and append. We run the benchmark with 25,000 files,
100 subdirectories, and 20K transactions, which results in a data size of about 400 MB.
The NetNews benchmark is developed by Karl Swartz to simulate the workload of a NetNews server
[33]. It creates a large number of files representing news articles, and deletes old articles by replaying traces
that were collected from a live server. This benchmark differs from the other benchmarks in that it has very
little locality of reference, and its large data set (2 GB) overwhelms the file system buffer cache [27].
4 Performance Results
We first compare the IDE drive with the SCSI drive on various benchmarks. Our results indicate that
IDE is faster on a sequential workload, but not on a random workload. We then analyze the results and study
four factors that affect performance: concurrency (tagged queuing), scheduling, file system features, and
parallelism (with multiple disks). We find that although IDE disks are slower on the random workload, the
performance deficiencies can be mitigated with judicious system design choices.
4.1 Single Disk Performance
Table 4 compares the performance of IDE and SCSI disks on the various benchmarks, and computes the
percentage differences in the last column. We use the Unix fast file system (FFS) [18] for the application
benchmarks. In this table we use a tag setting of 32 in the driver.
Benchmarks IDE SCSI % Perf. Diff
Sequential 31.6 MB/sec 15.8 MB/sec -100%
Interleave 23.5 MB/sec 17.4 MB/sec -35%
Micro benchmark
Random 1.5 MB/sec 2.5 MB/sec 40%
Sequential 27.1 MB/sec 19.2 MB/sec -41%
Interleave 19.2 MB/sec 16.8 MB/sec -14%
Micro benchmark
Random 1.4 MB/sec 2.1 MB/sec 33%
1 job 10.1 sec 12.3 sec -18%
5 jobs 78.2 sec 63.8 sec 23%
10 jobs 178.5 sec 129.4 sec 38%
20 jobs 377.1 sec 283.0 sec 33%
SSH 88.5 sec 74.9 sec 18%
PostMark 502 sec 387 sec 30%
NetNews 1100 sec 809 sec 36%
Table 4: SCSI versus IDE Performance.
The micro benchmark results indicate that the IDE disk is about 33% and 40% slower on random writes
and reads, respectively. This is consistent with the disk specifications in Table 3: a small random I/O
operation is dominated by the seek time [35, 6], and the random I/O performance difference between the IDE
and SCSI disks is similar to the ratio of disk seek times (36% and 43% for read and write, respectively). The
IDE disk is significantly faster (40-100%) than the SCSI disk on benchmark workloads with highly
sequential access patterns: this result is also consistent with Table 3.
On the application benchmarks, the SCSI disk is 18% (SSH) to 36% (NetNews) faster than the IDE disk.
Since these benchmarks have a large number of synchronous writes to random addresses, and these
experiments use the Unix FFS file system [27], the faster performance of the SCSI disk is consistent with its
faster seek time. Because the SSH benchmark is less I/O bound, it shows a smaller advantage for SCSI.
4.2 Factors Affecting Disk Performance
In Section 4.1 we have seen that the IDE disk performs well for sequential access, but not as well as the
SCSI disk for random access. We now investigate the effectiveness of four approaches that attempt to
improve the random-access performance of the IDE drive. In brief, these approaches are as follows.
In Section 4.2.1 we examine how the random-access performance varies depending on the degree of
concurrency (tagged queuing). I/O concurrency enables the drive to reorder requests to reduce the seek time.
Our results indicate that a tag size of 4 appears adequate for a single disk for these application benchmarks,
so IDE’s maximum tag limitation of 32 (versus 256 for SCSI) does not impair performance.
In Section 4.2.2 we investigate whether I/O scheduling in the host is an adequate substitute for tagged
queuing and scheduling in the disk, as suggested by prior disk scheduling studies [26, 12]. If we compare
ideal conditions for the host-based elevator scheduling (thousands of dirty pages in the buffer pool) with
ideal conditions for disk scheduling (tagged queuing with concurrency > 32), the random I/O micro
benchmark indicates that disk scheduling is significantly more effective than host scheduling. But the
application benchmarks show little performance difference between host-based and disk-based I/O
scheduling¾these benchmarks do not generate sufficiently high concurrency to favor either scheduler.
In Section 4.2.3 we evaluate the impact of two file system enhancements, Soft Updates (which reduces
synchronous metadata writes by careful write ordering) and Journal file system (which performs sequential
log writes for metadata updates). The results indicate that, when running benchmarks on the Soft Updates or
Journal file systems instead of FFS, the IDE and SCSI drives have similar performance (under certain
conditions described in Section 4.2.3). We use a novel low-level SCSI trace approach to explain the
qualifying conditions.
In Section 4.2.4 we explore using disk arrays [21] with random I/O workloads. We present results
obtained on hardware SCSI and IDE RAID arrays, and on the Vinum software RAID [15] on FreeBSD. Our
measurements indicate that IDE RAID hardware is now faster than host-attached SCSI disks or entry-level
SCSI RAID arrays.
We now elaborate on these topics.
4.2.1 Disk Concurrency
Table 5 and Table 6 summarize the performance of SCSI and IDE disks for various tag, scheduler, and
file system settings. We first study disk concurrency using the default FFS file system, with I/O scheduling
enabled in both the disk drive and the kernel.
We observe from Table 5 that there is no improvement in performance for a sequential workload with
higher concurrency (i.e. larger number of tags). This is expected since sequential I/O is already sorted in an
optimal manner. Concurrency improves the performance of SCSI disks on random I/O micro benchmarks by
25-50%, but for most application benchmarks by only 3-5%. We observe very little improvement in
performance beyond 4 tags, and no improvement in performance beyond 64 tags. To confirm that the
increase in tags does result in higher concurrency at the disk, we measure the average queue depth in the
SCSI disk using a SCSI bus analyzer. With 64 tags, we observe that the average number of outstanding
requests at the disk is 60 for reads and 46 for writes, yet with 4 tags the disk has already reached its peak rate
(220 requests/sec for 8KB random I/O).
For the IDE disk, tagged queuing gives improvements of 15-40% on random reads, and no improvement
on random writes. This latter result is suggestive of problems with IDE tagged queuing, which is
investigated in more detail in Section 4.2.2. Even with tagged queuing, IDE random read performance is
nearly 50% worse than the corresponding SCSI performance. We conclude that tagged queuing in its current
state is insufficient to overcome IDE’s larger seek latencies.
Scheduler Read Write Read WriNo. of te
Sequential Interleave Random Sequential Interleave Random Sequential Interleave Random Sequential Interleave Random
none 30.8 24.8 1.3 25.1 18.5 1.4 16.9 21.0 1.6 21.4 19.0 1.6
4 31.9 24.7 1.6 26.7 17.4 1.4 15.0 18.6 2.2 19.6 17.8 2.0
32 Both 31.6 23.5 1.5 27.1 19.2 1.4 15.8 17.4 2.5 19.2 16.8 2.1
64 16.0 15.8 2.4 19.1 15.8 2.1
(Not Supported in IDE)
15.8 15.2 2.4 19.1 15.6 2.1
None 31.4 1.9 0.9 23.3 10.7 1.1 14.4 1.8 1.1 19.4 0.9 1.0
4 32.7 2.0 1.1 24.2 10.7 1.1 14.1 1.9 1.5 18.2 1.7 1.6
32 Disk Only 31.6 2.0 1.3 24.5 10.5 1.1 14.4 9.4 2.2 19.3 11.8 1.8
64 14.1 8.9 2.3 19.2 10.5 1.9
(Not Supported in IDE)
14.8 8.9 2.3 19.0 10.4 1.9
None 30.7 24.4 1.3 24.3 18.2 1.4 16.5 21.0 1.6 21.1 18.7 1.6
4 32.4 24.7 1.6 24.3 17.0 1.4 14.7 18.9 1.8 19.7 17.8 1.6
32 Host Only 29.4 24.5 1.5 26.7 18.1 1.4 13.8 15.0 1.7 19.1 12.3 1.6
64 14.1 11.1 1.7 18.2 8.9 1.5
(Not Supported in IDE)
14.3 11.1 1.7 18.4 9.0 1.5
None 32.4 1.8 0.9 23.0 10.6 1.1 15.3 1.8 1.1 19.7 0.9 1.0
4 32.2 1.9 1.1 24.7 10.5 1.1 14.4 1.4 1.2 18.3 0.9 1.0
32 None 31.1 2.5 1.3 25.8 10.5 1.1 14.7 1.5 1.2 17.8 0.9 1.0
64 14.6 1.5 1.2 18.2 0.9 1.0
(Not Supported in IDE)
14.5 1.5 1.2 17.9 0.9 1.0
Table 5: Micro benchmark Results with Different Tag and Scheduler Settings. Results are reported in Mbyte/sec. Shaded columns under IDE contain
questionable results that may reflect bugs in queued DMA features of IDE disks¾see text.
Unix FFS (Sync Metadata and Async Data Update) Soft Updates (Async Metadata and Data Update)
Of Tags
SDET SSH NetNews PostMark SDET SSH NetNews PostMark SDET SSH NetNews PostMark SDET SSH NetNews PostMark
none 376.1 86.8 1100 500 292.2 80.9 840 406 19.8 63.5 837 199 20.0 64.2 658 181
8 (Not Supported by Driver) 282.7 80.3 809 383 (Not Supported by Driver) 19.5 63.9 574 158
374.5 86.6 1100 502 283.0 80.3 809 387 19.9 66.5 835 202 20.0 63.7 574 166
none 399.0 88.2 1234 571 348.2 83.3 971 485 20.5 64.0 931 250 19.4 65.9 824 248
8 (Not Supported by Driver) 302.8 81.1 855 407 (Not Supported by Driver) 20.3 64.4 610 182
Disk Only
399.9 87.9 1234 573 291.6 81.3 840 402 20.2 67.1 1001 252 20.4 64.5 585 173
none 378.5 87.7 1020 500 295.8 81.3 841 405 19.9 63.6 833 200 19.8 63.8 660 174
8 (Not Supported by Driver) 287.2 81.4 828 402 (Not Supported by Driver) 20.2 64.2 652 172
Host Only
380.9 87.5 1027 501 301.2 81.4 866 431 20.3 66.8 841 201 19.7 63.9 741 208
none 471.8 94.8 1141 573 345.6 83.1 974 485 19.6 68.2 940 249 21.5 65.7 823 249
8 (Not Supported by Driver) 333.7 83.1 950 471 (Not Supported by Driver) 20.7 65.9 801 245
460.2 95.0 1139 572 338.1 83.1 955 471 21.7 71.2 939 249 19.8 65.8 805 246
Table 6: Application Benchmark Results. All results are seconds of elapsed time. We vary the number of tags, disk or host scheduler, and type of file system.
Due to space constraints we report SDET results only for a script concurrency of 20, and only the total elapsed time for SSH. Note that the maximum number of
tags for the IDE disk is 32, versus 64 for the SCSI disk.
4.2.2 Disk Scheduling
Scheduling may be performed at the disk, which has intimate knowledge of the disk geometry.
Alternatively, scheduling may occur within the device driver or kernel, either of which may have access to a
larger pool of schedulable requests in the form of dirty blocks residing in a buffer cache. Throughout this
paper we refer to the former as disk scheduling, and the latter as host scheduling.
We first make the obvious observation that it is crucial to have some type of scheduler (disk, host or
both) in the system. From Tables 5 and 6, we see that for many of the benchmarks, having both schedulers
provides a substantial benefit over no scheduler, for both SCSI and IDE disks. We now compare the
performance of host and disk schedulers. We observe from Table 5 and 6 that when concurrency is below 32,
the host scheduler consistently out-performs the disk scheduler. This is expected, because the host scheduler
is working on a larger pool of I/O requests (maximum of 4096 versus 32 on disk). However, when the disk
has sufficient concurrency, it can outperform the host scheduler. For example, we see from Table 5 that on
random I/O, the SCSI disk scheduler outperforms the host scheduler by 13-35% when the concurrency
exceeds 32. In the SCSI application benchmark results in Table 6, we see less drastic differences: the disk
scheduler outperforms the host scheduler for NetNews by 12%. (SSH and SDET show less improvement as
they are less I/O bound.) On PostMark and NetNews, the performance gap is more obvious on the Soft
Update file system than on FFS (20-27% versus 3-12%). This is because synchronous I/O dominates the
application performance on FFS, whereas the Soft Update file system exploits a large pool of asynchronous
buffers in both the host and disk schedulers.
A non-intuitive result is that the host scheduler’s performance on SCSI disk decreases with increasing
number of tag (e.g. random and interleave macro benchmarks). This is due to a complex interaction between
the host scheduler and disk’s tag queuing mechanism. We explain a simple scenario in Figure 1, which shows
a host scheduler with a buffer size of 2 scheduling I/O requests. The shaded boxes represent new requests,
which alternate between the interleaved addresses. Figure 1a shows that when there is no tagged queuing, the
disk sees more sequential requests (i.e. 100, 101, 102, N+100, N+101, N+102...). This schedule incurs less
costly disk seeks than that generated by a host scheduler with tagged queuing enabled (see Figure 1b). The
latter schedule incurs more disk seeks since it alternates between interleaved addresses (i.e. 101, N+100, 102,
N+101...). In general, it is crucial that tag queuing is only enabled when the disk scheduler is active.
The results for disk scheduling in an IDE disk are puzzling. We fail to see the expected performance
increase of the interleaved micro benchmarks with increasing numbers of tags under disk scheduling. Also,
there is no performance difference between disk scheduling and no scheduling. These results are indicative
of problems in the current implementation of tagged queuing DMA, either in FreeBSD or in the disk
firmware. Instrumentation shows that the disk is not performing any scheduling for interleaved or random
writes. In the case of interleaved reads, traces indicate that requests are initially scheduled by the disk, but
revert to their original interleaved ordering after a few hundred have been satisfied. Note to referees: We
are working with the FreeBSD IDE driver author to locate the source of this problem (i.e. driver, IDE
controller or IBM disk?) and expect to have conclusive results in the final version of this paper.
Because our experiments use the default I/O scheduler in FreeBSD, our results do not reflect the full
potential of host scheduling. Scheduling algorithms that utilize detailed knowledge of physical data layout on
disk, and that accurately track the disk arm position, can outperform BSD's elevator algorithm [12, 26, 36].
T1 100 101
T2 N+100 101
T3 N+100 102
T4 N+100 N+101
T5 103 N+101
T6 103 N+102
Time Disk Addresses RequestServed
100 101
N+100 102
103 N+101
N+102 104
105 N+103
N+104 106
Disk Addresses RequestServed
(a) Host Scheduler, no Tagged Queuing (b) Host Scheduler, # of Tag=2
Figure 1: I/O Schedule as Seen by the Disk During an Interleave Micro Benchmark.
4.2.3 File System Designs
Table 6 presents application benchmark results comparing FFS with Soft Updates on both IDE and SCSI
disks. We observe that on FFS, SCSI consistently out-performs IDE (15-47%) on most application
benchmarks (except SSH which is less I/O bound). On the Soft Updates file system, SCSI and IDE disks
have comparable performance for most benchmarks except NetNews: The NetNews buffer footprint exceeds
the system buffer pool, so the application is occasionally blocked while the buffer pool is cleaned. This
cleaning causes synchronous random writes, a workload for which the SCSI disk has significantly better
performance. We also note that on the Soft Updates file system, higher concurrency settings increase the
performance advantage of SCSI over IDE (e.g., from 10% to 22% on PostMark). This is mainly due to the
buggy IDE tagged queuing, so we expect this advantage to diminish in the future. On balance, Soft Updates
helps compensate for the slower seeks of the IDE disk.
We now explore the performance of Soft Updates and journaling in more detail by examining a lowlevel
trace of physical disk accesses during benchmark runs, as recorded by a SCSI bus analyzer. We limit
our discussion to the PostMark benchmark due to space constraints, but our observations are similar for the
SDET and NetNews benchmarks.
Figure 2 shows the sequence of disk accesses during a PostMark run on three file systems: FFS, Soft
Updates, and NTFS. Each point indicates the time and corresponding disk address of a physical disk write.
Figure 2a shows the SCSI trace during a PostMark run on FFS. The PostMark benchmark has 3 phases:
create files, run transactions, and delete files. The create phase touches inodes in many cylinder groups [19],
thus we see the gradual march from the first to Nth cylinder group, where N is determined by the data set.
The run phase updates data created during the create phase, and thus we see disk activity from the first to the
Nth cylinder group. Because the run phase also creates files, we see additional activity above the Nth cylinder
group (i.e. disk address from 180000-240000). The vertical lines in the run phase represent update daemon
activities, occurring once every 30 seconds [19]. These writes are asynchronous and are sorted by the host
scheduler in ascending order to minimize disk seeks, and appear as vertical lines due to the compressed time
scale. The horizontal lines in Figure 2a represent synchronous metadata updates for each cylinder group.
These synchronous writes, spread over many cylinder groups, make random writes the dominant factor.
Thus, for this workload on FFS, IDE is much slower than SCSI.
Figure 2b shows the same experiment on the Soft Updates file system. This file system has very few
synchronous metadata writes [27], and thus we do not see any horizontal lines. Almost all writes are
asynchronous and can be scheduled nicely (thus the straight vertical lines). The delete phase in the Soft
Update file system is considerably faster than on FFS, because most files are created and deleted entirely
within the buffer cache, so relatively little physical disk I/O occurs [27]. With writes not being a factor, the
determining factor for the Soft Update file system is concurrency (i.e. how fast the disks absorb the sorted
asynchronous writes). IDE and SCSI are somewhat comparable with no tagged queuing, but current IDE
systems improve less than SCSI does when tagged queuing is enabled, as seen in Table 6.
Our measurements show that IDE is faster (10%) than SCSI on the PostMark benchmark when running
on the Windows NTFS file system. NTFS is a journaling file system, so its metadata write activity is largely
sequential. Consequently, IDE has comparable performance to SCSI, as seen in Figure 2c. We observe that
the writes are clustered in 3 regions. The middle region contains asynchronous writes to the file data. The
two horizontal lines in Figure 2c represent sequential writes to the log files in the Master File Table (MFT)
and the MFT mirror [30] (magnified view in Figure 2d). The MFT is located near the start of the disk, and
the MFT mirror is located in the middle of the disk. Because the sequential writes alternate between these
two regions, the I/O accesses resemble the interleaved I/O modeled in Table 5. We know that SCSI and IDE
have comparable performance for that access pattern.
In summary, we find that file system design has great potential to enable IDE to achieve performance
comparable to SCSI. In journaling or log-structured file systems or with workloads dominated by sequential
or interleaved writes, IDE performs comparably to SCSI. IDE also performs well under Soft Updates.
Disregarding tagged queuing, IDE achieves 80% of SCSI’s performance for NetNews and 90% for
PostMark, and would be comparable to SCSI in this environment if IDE’s tagged queuing performed as
PostMark SCSI Trace (FreeBSD, FFS)
0 100 200 300 400
Time (seconds)
Disk Address (Sectors)
Create Files Transactions Delete Files
PostMark SCSI Trace (FreeBSD, Soft Updates)
0 20 40 60 80
Time (seconds)
Disk Address (Sectors)
Create Files Transactions
PostMark SCSI Trace (Windows NT, NTFS)
0 100 200 300 400 500
Time (seconds)
Disk Address (Sectors)
Files Transactions
(a) (b)
PostMark MFT Trace (Windows NT, NTFS)
0 100 200 300 400 500
Disk Address
PostMark MFTMirror Trace (Windows NT, NTFS)
0 100 200 300 400 500
Disk Address
Figure 2: SCSI Trace of PostMark Benchmark. The I/O traces are collected from the SCSI bus using a SCSI bus analyzer.
4.2.4 Parallelism
In this section we explore the performance of disk arrays. The software disk array is configured as
RAID 0 (striping only, no parity computation) since a RAID 5 configuration would result in a bottleneck
caused by parity I/Os, masking the IDE vs. SCSI performance issues that we are investigating. The
hardware array and the software array both use a 64KB stripe size.
Software Disk Array (Vinum) Hardware Disk Array
IDE SCSI % Faster IDE SCSI % Faster
Sequential 6.9 23.9 -246% 15.1 35.2 -133%
Interleave 7.2 23.5 -226% 15.4 25.8 -68%
Write Micro
(MB/sec) Random 5.4 4.2 22% 3.8 3.3 13%
SDET 303.1 244.9 -19% 21.5 43.1 100%
SSH 86.8 78.3 -10% 64.6 65.1 1%
NetNews 542 420 -23% 410.0 326.0 -20%
FreeBSD Unix FFS
PostMark 205 237 16% 57.0 106.0 86%
SDET 16 16.9 6% 16.5 16.2 -2%
SSH 62.5 64.1 3% 63.2 63.3 0%
NetNews 344 255 -26% 309.0 301.0 -3%
FreeBSD Soft
Update File System
PostMark 74 96 30% 49.0 82.0 67%
Table 7: SCSI versus IDE Disk Array Performance. The hardware disk array has 8 disks configured in
RAID5 with 7+1 spare. The software disk array has 4 disks configured in RAID 0 (striping only without
redundancy). The % Faster column compares IDE versus SCSI performance.
Table 7 compares the performance of software and hardware disk arrays for the write micro benchmark and
for the application benchmarks running on the FFS and the Soft Updates file systems. We omit reporting
the read micro benchmarks and other variables (Windows NT, tag settings) as they merely reinforce the
The write micro benchmark results indicate that SCSI is faster on sequential I/O, but slower on random
I/O. These results contradict the single disk results in Table 5. These differences are largely due to system
design factors. In particular, both the hardware and software IDE disk arrays have lower bus contention
because they use 1 bus per IDE disk, whereas the SCSI disk array has one (Vinum software array) or two
(hardware RAID) SCSI buses in total. The IDE hardware RAID’s slower sequential performance may
reflect an inefficient I/O scheduling algorithm, as other vendors (e.g. 3ware and JetStor) have demonstrated
much greater sequential bandwidth.
The micro benchmark results provide clues into the macro benchmark results. We first look at the
software IDE array. The software IDE array is mostly slower when running on the FFS file system. Both
SDET and SSH have a small buffer footprint, and their I/Os are smaller than the stripe unit (2KB versus
64KB) and tend to concentrate on several cylinder groups in a single disk. Thus the benchmark workload
degenerates into random I/O on a single disk, so the SCSI array performs better. The single disk bottleneck
can be mitigated with NVRAM to absorb metadata updates for hot cylinder groups. Both hardware disk
arrays have large (128 MB) buffer caches, thus the remaining performance obstacle is random writes. The
software IDE array is faster than the SCSI array on configurations that have large amounts of asynchronous
random writes, thus it performs better on the SDET, SSH, and PostMark benchmarks when using the Soft
Update file system. NetNews’ buffer footprint exceeds the system buffer pool and thus is dominated by
metadata writes (both random and interleaved), thus it exhibits the worst performance under both file
system configurations. Since the IDE hardware disk array has faster random writes, it performs better on
most benchmarks.
We can tailor a disk array to take advantage of IDE technology. In particular, IDE controllers are
significantly less expensive than SCSI controllers (e.g. the Promise PDC20267 chip is at least 6 times less
expensive than Adaptec’s 78xx controller), so one can eliminate I/O bus contention in an IDE array by
using a separate bus for each disk. The disk array can also export a SCSI interface to the host to overcome
the lack of concurrency at the IDE interface.
5 Related Work
5.1 IDE versus SCSI Test Reports
The debate over the relative performance of IDE and SCSI has been fought hard and long. The Internet
and USENET (e.g. see comp.periphs.scsi) are replete with comparisons of the two technologies.
The online PC Guide [22] provides an excellent overview of SCSI and IDE. The site discusses the
relevant metrics of comparison, while stopping short of a full-fledged quantitative evaluation. Another
excellent source for SCSI and IDE information is the book by Schmidt [25].
Cardenas and Catena [3] compare popular drive performance characteristics and costs. Their
benchmark highlights the performance of a wide range of disks under expected usage in a digital audio
recording and production environment. Their workload is largely sequential and has real-time constraints.
Their main conclusion is that for digital audio, the disk media transfer rate is the most important factor.
They do not treat accesses to multiple devices or general-purpose workloads.
Murakami provides another good source on the performance characteristics of modern SCSI and IDE
disk drives [20]. He examines the performance of SCSI and IDE disks in single disk and RAID
configurations under the Linux operating system. He measures the raw device bandwidth using the Linux
hdparam program, and file system performance using the bonnie and bonnie++ workloads. The later
workloads invoke various file system operations like sequential I/O, random seeks, and directory
operations. Murakami’s results indicate that among IDE and SCSI drives of comparable specifications, IDE
performance is comparable or faster. The difference is especially pronounced for write operations.
Murakami provides no insight into his results. We suspect that the difference in write performance may be
a consequence of a disabled write cache (WCE [25]) on the SCSI drive.
Stam [31] examines SCSI and IDE disks with similar specifications in a Windows environment. He did
not yield conclusive disparities, although SCSI provides performance superior to IDE in the presence of
heterogeneous devices such as a CD-ROM and disk. No explanation is given for any of his results.
Martin and Scholl [17] shows that a 10,000 RPM Wide Ultra SCSI disk achieves a 50-60%
improvement over a “Best Ultra DMA” IDE disk on the ThreadMark and WinBench benchmarks. The
paper touts SCSI’s ability to issue multiple I/O requests through tagged queuing, but leaves the details of
the experiments and the impact of tagged queuing insufficiently well specified to enable the replication of
these results.
Stone [32] utilizes the same ThreadMark and WinBench benchmarks as Martin and Scholl, but obtains
a completely different result. Promise’s FastTrak IDE RAID controller is contrasted with IDE and Ultra
Wide SCSI single disk configurations. The results indicate that IDE disks are faster than SCSI on these
benchmarks. FastTrak’s speedup over its competitors is not explained.
Recent work at Microsoft has targeted I/O performance. Riedel et al. [24] investigate the performance
of the Windows NT File System, and provide a thorough discussion of bus, controller, and file system
overheads, but only address sequential I/O on SCSI disks. Follow-on work [4] continues in a similar vein,
but also incorporates studies of random workloads and IDE. This paper suggests that aggregating IDE disks
through IDE RAID leads to a viable, inexpensive, and efficient storage system. The authors report that IDE
has a 25% higher ‘I/Os per second per dollar’ ratio than SCSI for random I/O, while approaching the
performance of SCSI for sequential workloads. They attain linear improvement in read throughput and
scalability for writes using up to three IDE disks with 3ware’s IDE RAID adapter card [1]. Measurements
are obtained via micro benchmarks on Windows NT and 2000.
We find that much of the above work suffers from an incomplete specification of the test environment
and system configuration. Some studies compare widely different products. Most studies also do not use
realistic application benchmarks and rely on micro benchmarks alone. Finally, most studies do not provide
sufficient insight to explain their results. By contrast, in this paper we present a thorough comparison of
SCSI and IDE across a range of popular application benchmarks. We use similar disks throughout the
comparisons and provide a full description of the system configuration. Further, low-level micro
benchmarks, kernel instrumentation, and SCSI analyzer traces illuminate our findings.
5.2 Disk Performance Studies
Kerns provides a detailed introduction to SCSI tagged queuing, and assesses the importance of this
capability via micro benchmark studies [14]. His results suggest that a small number of tags (<32) is
sufficient to achieve good performance.
Besides concurrency, disk scheduling is also crucial in reducing seek latencies [26, 12]. Scheduling
may be performed at the disk, which has intimidate knowledge of the disk geometry, or within the kernel,
which may have a large pool of schedulable requests. The tradeoff between these two approaches is
studied in detail in [26], where the authors show the merits of host scheduling. Recent work on extensible
kernels also advocates fine-grain control of disk resources for maximum flexibility and adaptivity (e.g.,
Nemesis [2]).
The file system design has a significant effect on disk performance as it determines the data layout and
physical access patterns. Unix FFS [18] statically allocates regions of the disk for inodes, and tries to locate
data and metadata in rotationally optimal positions. However, metadata operations still incur significant
seek and rotational latencies [9]. Moreover, FFS typically writes its metadata synchronously to disk to
maintain file system integrity. Many recent studies reduce synchronous and non-sequential accesses at the
file system (see [27] for an excellent introduction). In this paper we evaluated two modern file systems.
Soft Updates [9] tracks dependencies between metadata blocks such that metadata writes may be delayed,
thus significantly reducing the number of synchronous I/Os and allowing for more effective scheduling. A
journaling file system writes its metadata sequentially to disk, greatly reducing disk seeks [30].
6 Conclusions
We have presented a thorough examination of IDE and SCSI performance through a combination of
micro benchmarking and macro benchmarking. The principal independent variables for the benchmarking
are workload factors such as sequentiality, locality, and read/write ratio, and system aspects such as I/O
concurrency (as enabled by tagged queuing), disk scheduling in the host or on the disk, file system features
such as journaling and soft updates, and I/O parallelism via disk arrays.
The IDE disks that we measured are generally faster than the SCSI disks for sequential I/O, but slower
for random I/O. Our experimental results indicate that we can mitigate the random I/O handicap of IDE by
appropriate choices with respect to the system aspects mentioned above.
We also measured the performance of software and hardware RAID arrays built from IDE disks and
from SCSI disks. The IDE and SCSI arrays exhibit similar performance. We note several techniques that
may give substantial additional performance improvements to the IDE array.
7 Acknowledgements
We would like to thank Liddy Shriver for suggesting that we compare host and disk scheduling.
Along with Liddy, Margo Seltzer did the initial work in this area and allowed us to follow in her tracks.
We are indebted to Keith Smith for help in acquiring and running the benchmarks.
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