CSE 221: System Measurement Project (Winter 2016)
- Draft of Intro, Machine Description, and CPU Operations:
Tuesday, February 2 at 8/9:30am (start of class)
- Draft of Memory Operations:
Tuesday, February 23 at 8/9:30am (start of class)
- Final report with all measurements plus code:
Monday, March 14 at noon
In building an operating system, it is important to be able to
determine the performance characteristics of underlying hardware
components (CPU, RAM, disk, network, etc.), and to understand how
their performance influences or constrains operating system services.
Likewise, in building an application, one should understand the
performance of the underlying hardware and operating system, and how
they relate to the user's subjective sense of that application's
"responsiveness". While some of the relevant quantities can be found
in specs and documentation, many must be determined experimentally.
While some values may be used to predict others, the relations between
lower-level and higher-level performance are often subtle and
In this project, you will create, justify, and apply a set of
experiments to a system to characterize and understand its
performance. In addition, you may explore the relations between some
of these quantities. In doing so, you will study how to use
benchmarks to usefully characterize a complex system. You should also
gain an intuitive feel for the relative speeds of different basic
operations, which is invaluable in identifying performance bottlenecks.
You have complete choice over the operation system and hardware
platform for your measurements. You can use your laptop that you are
comfortable with, an operating system running in a virtual machine
monitor, a smartphone, a game system, or even a supercomputer.
You may work either alone or 2–3 person groups.
Groups do the same project as individuals. All members receive the
same grade. Note that working in groups may or may not make the
project easier, depending on how the group interactions work out. If
collaboration issues arise, contact your instructor as soon as possible:
flexibility in dealing with such issues decreases as the deadline
This project has two parts. First, you will implement and perform
a series of experiments. Second, you will write a report documenting
the methodology and results of your experiments. When you finish, you
will submit your report as well as the code used to perform your
Your report will have a number of sections including an
introduction, a machine description, and descriptions and discussions
of your experiments.
Describe the goals of the project and, if you are in a group, who
performed which experiments. State the language you used to implement
your measurements, and the compiler version and optimization settings
you used to compile your code. If you are measuring in an unusual
environment (e.g., virtual machine, Web browser, compute cloud, etc.),
discuss the implications of the environment on the measurement task
(e.g., additional variance that is difficult for you to control for).
Estimate the amount of time you spent on this project.
2) Machine Description
Your report should contain a reasonably detailed description of the
test machine(s). The relevant information should be available either
from the system (e.g.,
on Linux, System Profiler on Mac OS X, the
x86 instruction), or online
. Gathering this information
should not require much work, but in explaining and analyzing your
results you will find these numbers useful. You should report at
least the following quantities:
- Processor: model, cycle time, cache sizes (L1, L2, instruction,
- Memory bus
- I/O bus
- RAM size
- Disk: capacity, RPM, controller cache size
- Network card speed
- Operating system (including version/release)
Perform your experiments by following these steps:
- Estimate the base hardware performance of the operation and cite
the source you used to determine this quantity (system info, a
particular document). For example, when measuring disk read
performance for a particular size, you can refer to the disk
specification (easily found online) to determine seek, rotation, and
transfer performance. Based on these values, you can estimate the
average time to read a given amount of data from the disk assuming no
software overheads. For operations where the hardware performance
does not apply or is difficult to measure (e.g., procedure call),
state it as such.
- Make a guess as to how much overhead software will add to the
base hardware performance. For a disk read, this overhead will
include the system call, arranging the read I/O operation, handling
the completed read, and copying the data read into the user buffer.
We will not grade you on your guess, this is for you to test your
intuition. (Obviously you can do this after performing the experiment
to derive an accurate "guess", but where is the fun in that?) For a
procedure call, this overhead will consist of the instructions used to
manage arguments and make the jump. Finally, if you are measuring a
system in an unusual environment (e.g., virtual machine, compute
cloud, Web browser, etc.), estimate the degree of variability and
error that might be introduced when performing your measurements.
- Combine the base hardware performance and your estimate
of software overhead into an overall prediction of performance.
- Implement and perform the measurement. In all cases, you should
run your experiment multiple times, for long enough to obtain
repeatable measurements, and average the results. Also compute the
standard deviation across the measurements. Note that, when measuring
an operation using many iterations (e.g., system call overhead),
consider each run of iterations as a single trial and compute the
standard deviation across multiple trials (not each individual
- Use a low-overhead mechanism for reading timestamps. All modern
processors have a cycle counter that applications can read using a
Searching for "rdtsc" in Google, for instance, will provide you with a
plethora of additional examples. Note, though, that in the modern age
of power-efficient multicore processors, you will need to take
additional steps to reliably use the cycle counter to measure the
passage of time. You will want to disable dynamically adjusted CPU
frequency (the mechanism will depend on your platform) so that the
frequency at which the processor computes is determinstic and does not
vary. Use "nice" to boost your process priority. Restrict
your measurement programs to using a single core.
In your report:
- Clearly explain the methodology of your experiment.
- Present your results:
- For measurements of single quantities (e.g., system call
overhead), use a table to summarize your results. In the table
report the base hardware performance, your estimate of software
overhead, your prediction of operation time, and your measured
- For measurements of operations as a function of some other
quantity, report your results as a graph with operation time on the
y-axis and the varied quantity on the x-axis. Include your estimates
of base hardware performance and overall prediction of operation time
as curves on the graph as well.
- Discuss your results:
- Cite the source for the base hardware performance.
- Compare the measured performance with the predicted performance.
If they are wildly different, speculate on reasons why. What
may be contributing to the overhead?
- Evaluate the success of your methodology. How accurate
do you think your results are?
- For graphs, explain any interesting features of the curves.
- Answer any questions specifically mentioned with the operation.
- At the end of your report, summarize your results in a table for
a complete overview. The columns in your table should include
"Operation", "Base Hardware Performance", "Estimated Software
Overhead", "Predicted Time", and "Measured Time". (Not required for
- State the units of all reported values.
Do not underestimate the time it takes to describe your methodology
- CPU, Scheduling, and OS Services
- Measurement overhead:
Report the overhead of reading time, and report
the overhead of using a loop to measure many iterations
of an operation.
- Procedure call overhead:
Report as a function of number of integer arguments from 0-7.
What is the increment overhead of an argument?
- System call overhead: Report the cost of a minimal system
call. How does it compare to the cost of a procedure
call? Note that some operating systems will cache the
results of some system calls (e.g., idempotent system
calls like getpid), so only the first call by a process
will actually trap into the OS.
- Task creation time: Report the time to create and run both
a process and a kernel thread (kernel threads run at
user-level, but they are created and managed by the OS;
e.g., pthread_create on modern Linux will create a
kernel-managed thread). How do they compare?
- Context switch time: Report the time to context switch
from one process to another, and from one kernel thread to
another. How do they compare? In the past students have
found using blocking pipes to be useful for forcing
- RAM access time: Report latency for individual integer
accesses to main memory and the L1 and L2 caches. Present
results as a graph with the x-axis as the log of the size
of the memory region accessed, and the y-axis as the
average latency. Note that the lmbench paper is a good
reference for this experiment. In terms of the lmbench
paper, measure the "back-to-back-load" latency and report
your results in a graph similar to Fig. 1 in the paper.
You should not need to use information about the machine
or the size of the L1, L2, etc., caches when implementing
the experiment; the experiment will reveal these sizes.
In your graph, label the places that indicate the
different hardware regimes (L1 to L2 transition, etc.).
- RAM bandwidth: Report bandwidth for both reading and
writing. Use loop unrolling to get more accurate results,
and keep in mind the effects of cache line prefetching
(e.g., see the lmbench paper).
- Page fault service time:
Report the time for faulting an entire page from disk (mmap
is one useful mechanism).
Dividing by the size of a page, how does it compare to the
latency of accessing a byte from main memory?
- Round trip time. Compare with the time to perform
a ping (ICMP requests are handled at kernel level).
- Peak bandwidth.
- Connection overhead: Report setup and tear-down.
Evaluate for the TCP protocol. For each quantity, compare both
remote and loopback interfaces. Comparing the remote and
loopback results, what can you deduce about baseline network
performance and the overhead of OS software? For both round
trip time and bandwidth, how close to ideal hardware performance
do you achieve? What are reasons why the TCP performance does
not match ideal hardware performance (e.g., what are the
pertinent overheads)? In describing your methodology for the
remote case, either provide a machine description for the second
machine (as above), or use two identical machines.
- File System
- Size of file cache: Note that the file cache size is
determined by the OS and will be sensitive to other load on
the machine; for an application accessing lots of file
system data, an OS will use a notable fraction of main
memory (GBs) for the file system cache. Report results as a
graph whose x-axis is the size of the file being accessed
and the y-axis is the average read I/O time. Do not use a
system call or utility program to determine this metric
except to sanity check.
- File read time: Report for both sequential and random access
as a function of file size. Discuss the sense in which
your "sequential" access might not be sequential. Ensure
that you are not measuring cached data (e.g., use the
raw device interface). Report as a graph with a log/log
plot with the x-axis the size of the file and y-axis the
average per-block time.
- Remote file read time: Repeat the previous experiment for
a remote file system. What is the "network penalty" of
accessing files over the network? You can either
configure your second machine to provide remote file
access, or you can perform the experiment on a department
machine (e.g., APE lab). On these machines your home
directory is mounted over NFS, so accessing a file under
your home directory will be a remote file access
(although, again, keep in mind file caching effects).
- Contention: Report the average time to read one file
system block of data as a function of the number of
processes simultaneously performing the same operation on
different files on the same disk (and not in the file
During the quarter you will have read a number of papers
describing various system measurements, including V, Sprite,
microkernels, Scheduler Activations, LRPC, LFS, and IO-Lite. You may
find these papers useful as references.
In addition, other papers you may find useful for help with system
- John K. Ousterhout, Why
Aren't Operating Systems Getting Faster as Fast as Hardware?,
Proc. of USENIX Summer Conference, pp. 247-256, June 1990.
- J. Bradley Chen, Yasuhiro Endo, Kee Chan, David Mazieres,
Antonio Dias, Margo Seltzer, and Michael D. Smith, The
measured performance of personal computer operating systems,
Proc. of ACM SOSP, pp. 299-313, December 1995.
- Larry McVoy and Carl Staelin, lmbench:
Portable Tools for Performance Analysis, Proc. of USENIX Annual
Technical Conference, January 1996.
- Aaron B. Brown and Margo I. Seltzer, Operating
system benchmarking in the wake of lmbench: a case study of the
performance of NetBSD on the Intel x86 architecture, Proc. of ACM
SIGMETRICS, pp. 214-224, June 1997.
You may read these papers, or other references, for strategies on
performing measurements, but you may not examine code to copy or
replicate the implementation of a measurement. For example, reading
the lmbench paper is fine, but downloading and looking at the
lmbench code violates the intent of the project.
Finally, it goes almost without saying that you must implement all
of your measurements. You may not download a tool to perform the
measurements for you.
We will grade your project on the relative accuracy of your
measurement results (disk reads performing faster than the buffer
cache are a bad sign) as well as the quality of your report in terms
of methodology description (can we understand what you did and why?),
discussion of results (answering specific questions, discussing
unexpected behavior), and the writing (lazy writing will hurt your grade).
In the past, a frequent issue we see with project reports is that
they do not clearly explain the reasoning behind the estimates,
methodology, results, etc. As a result, we do not fully understand
what you did and why you did it that way. Be sure to explain your
reasoning as well.
As a first stage of the project, we would like you to submit an
early draft of the first part of the project. What should you cover
in the draft? The first two parts of the report (Introduction and Machine Description), and the first set of
operations (CPU, Scheduling, and
OS Services). For this step only submit a draft of the report,
not your code.
What percentage of the project grade does it form? It will only
be worth 5% of your grade. Why so little? The idea with the initial
draft is that it is primarily for your own benefit: it will get you
started on the project early, and it will give you a sense for how
long it will take you to complete the project by the end of the
quarter (in the past, students have reported that it has taken them
40-120 hours on the project). As a result, you should be able to
better budget your time as the end of the quarter arrives. How rough
can the draft be? Your call — again, this is primarily for your
In the second stage of the project, extend your report draft with
results for the second set of operations (Memory). Please also hand in your graded
report from the first stage for reference.
For the drafts, bring one hardcopy per group with you to class on
For the final project reports, submit them in pdf to your
instructor and TA via email (if your group spans both sections, send
to both). Submit your code as well.