CSE 221: System Measurement Project

Winter 2024

Deadlines

Overview

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 non-obvious.

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 can use a mainstream OS like Linux, MacOS, or Windows; a wide range of other Unix-based OSes (e.g., FreeBSD, NetBSD, OpenBSD, DragonFly BSD); or alternative OSes such as Fuschia, Haiku, or Plan 9/Inferno.

The project tasks originally had mainstream hardware platforms and operating systems in mind. But we also want to encourage you to do the project in more exotic platforms and environments. As a result, we can adapt the project tasks to the particular platform configuration you choose (e.g., if an unusual OS does not support a particular feature, such as a driver for your network card). Talk to us and we can adapt the project as needed.

You may work either alone or in 2–3 person groups. All groups do the same project, and 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 approaches.

This project has two aspects: you will implement and perform a series of experiments, and 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 experiments.

Report

Your report will have a number of sections including an introduction, a machine description, and descriptions and discussions of your experiments.

1) Introduction

2) Machine Description

1. Processor model, cycle time, cache sizes (L1 instruction and data, L2, L3)
2. DRAM type, clock, and capacity
3. Memory bus bandwidth
4. I/O bus type (e.g., PCIe 3.0), bandwidth
5. Disk
   • SSD: model, capacity, transfer rates, IOPs, latencies (if reported on spec)
   • Hard drive: model, capacity, RPM, transfer rates, latencies
6. Network card bandwidth
7. Operating system (including version/release)

If you are on an unusual platform, you may not be able to track down all of this information. In that case, just report "Unknown".

3) Experiments

1. 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 (found online) to determine performance characteristics. 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), you can leave hardware performance blank.
2. 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? Also, if your guesses are consistently accurate and essentially the same as your measured results, then we're going to be highly suspicious.) For a procedure call, this overhead will consist of the instructions used to manage arguments and make the jump. (Note that you do not need to track down the number of cycles required for each different kind of instruction. Instead, just track down a reasonable estimate of CPI for your CPU and multiply the CPI with your estimate of number of instructions.)

   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.

3. Combine the base hardware performance (when applicable) and your estimate of software overhead into an overall prediction of performance.
4. Implement and perform the measurement. In all cases, you should run your experiment multiple times, and for long enough to obtain repeatable measurements. You should examine the raw values of your samples to make sure that nothing unexpected is happening (e.g., a process context switch), and then compute a summary statistic across the samples. By default, compute the mean (average). To reduce the impact of unexplained outliers, you can also use the trimmed mean (for details see the hbench paper listed at the bottom of the page). 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 iteration).
5. Use a low-overhead mechanism for reading timestamps. All modern processors have a cycle counter that applications can read using a special instruction (e.g., rdtsc). 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 → More tips on measuring time

1. Clearly explain the methodology of your experiment.
2. Present your results:
   1. 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 (when applicable), your estimate of software overhead, your prediction of operation time, and your measured operation time. For units, use wall-clock time (e.g., microseconds) instead of cycles.
   2. 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. Where possible, include your estimates of base hardware performance and overall prediction of operation time as curves on the graph as well.
3. Discuss your results:
   1. Cite the source for the base hardware performance.
   2. Compare the measured performance with the predicted performance. If they are wildly different, speculate on reasons why. What may be contributing to the overhead?
   3. Evaluate the success of your methodology. How accurate do you think your results are?
   4. For graphs, explain any interesting features of the curves.
   5. Answer any questions specifically mentioned with the operation.
4. 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 the draft.)
5. State the units of all reported values.

Do not underestimate the time it takes to describe your methodology and results.

4) Operations

1. CPU, Scheduling, and OS Services
   1. Measurement overhead: Report the overhead of reading time, and report the overhead of using a loop to measure many iterations of an operation.
   2. Procedure call overhead: Report as a function of number of integer arguments from 0-7. What is the incremental overhead of an argument?
   3. 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.
   4. 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?
   5. 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 context switches. (For insight into why a context switch can be much more expensive than a procedure call, consider the evolution of the Linux kernel trap on x86.)

   For methodology examples see the "lmbench" and "hbench" papers listed below at the bottom of the project page. It is also important to keep in mind that measuring short execution sequences can be challenging since things like cache effects can skew the results; see the discussions in the lmbench (Sec 3.4) and hbench (Sec 2.1) papers for ways to reduce these effects, such as warming caches and manual loop unrolling.

2. Memory
   1. 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.

      The ideal case is that you do not need to use information about the machine or the size of the L1, L2, etc., caches when implementing the experiment, and that the experiment will reveal these sizes. However, modern CPU architectures are incredibly complex and have many optimizations in them that can lead to seemingly funky behavior (e.g., a fun exploration of why the value of the write (writing zeros) can lead to different performance).

      With this in mind, your experiments may not reveal nice, sharp boundaries like the original paper. Do the best you can and, while the boundaries may not be crystal clear in your experiments, state in the report where your results do indicate different hardware regimes (L1 to L2 transition, etc.).

   2. 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). (Also keep in mind that RAM bandwidth is likely limited by the DRAM and not the memory bus bandwidth.)
   3. 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?

3. Network
   
   1. Round trip time. Compare application-level round trip time with the time to perform a ping using the ping command (ICMP requests are handled at kernel level).
   2. Peak bandwidth.
   3. Connection overhead: Report setup and tear-down.

   Evaluate for the TCP protocol. For each quantity, compare both remote and loopback (localhost) 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.

4. File System
   
   1. 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.
   2. File read time: Report for both sequential and random access as a function of file size. Ensure that you are not measuring cached data when reading. The OS will cache file data by default when processes both read and write file data. Fortunately, OSes will generally provide mechanisms for managing the cache. On Linux, for instance, from the command line you can flush the entire cache (see /proc/sys/vm/drop_caches) or specific files (e.g., see oflag=nocache with the dd utility); programmatically, see the posix_fadvise system call. 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.
   3. 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).
   4. 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 buffer cache).

References

During the quarter you will have read a number of papers describing various system measurements, particularly in the second half of the quarter. You may find those papers on the reading list useful as references.

In addition, other papers you may find useful for help with system measurement are:

• 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 Mazières, 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. (Colloquially known as the "hbench" paper.)

• John Ousterhout, Always Measure One Level Deeper, Communications of the ACM, Vol. 61, No. 7, July 2018, pp. 74–83.

• Xiang (Jenny) Ren, Kirk Rodrigues, Luyuan Chen, Camilo Vega, Michael Stumm, and Ding Yuan, An Analysis of Performance Evolution of Linux's Core Operations, Proc. of the 27th ACM Symposium on Operating Systems Principles (SOSP 2019), pp. 554–569, October 2019.

• Gernot Heiser, Systems Benchmarking Crimes.

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.

Grading

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.

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.

You will submit two drafts of your project throughout the quarter as well as a final complete report at the end. For the first draft, you should cover the first two parts of the report (Introduction and Machine Description), and the first set of operations (CPU, Scheduling, and OS Services). For the second draft, extend your first draft with results for the second set of operations (Memory).

The first and second drafts will each be worth 10% of your overall project grade. Why so little? The idea with the drafts is that they are primarily for your own benefit: they will get you started on the project early, and 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. You can also update and add to the earlier sections of your report for the final report if we have feedback.

For the checkpoint drafts and the final report, submit them as pdf files in your github repo. Create a top-level directory "reports" in your repo, and place your documents in that directory with the names "first", "second", and "final".