Many individuals in the Linux community periodically express the belief that Linux “seems to be a faster environment” than Windows 95 and 98. However, it's difficult to find hard confirming evidence about Linux's speed, and little comparative performance data is readily available. As a result, many people considering adoption of Linux are probably unable to fully consider its performance in their decision. Of course, performance is only one of many factors that should be considered in evaluating an environment's suitability. For example, cost, reliability, usability and scalability are also important considerations.
In this article, we'll compare the computational performance for an application running under Linux and under Windows 95 and 98. We also examine if the application's performance under Linux can match performance under Windows NT. Last, we consider if the performance of Linux on the leading x86 CPU makes that environment a viable alternative to Linux on Intel.
We hope these test results provide some much-needed evidence of the potential for excellent computational performance in the Linux environment.
A number of measures have been used to compare CPU and system performance. Two well-known benchmarks for measuing raw CPU computational capability are SPECint and SPECfp. Overall system performance has been measured using a variety of techniques, such as POVray rendering (frame) rates, Quake frame rates, Business Winstone ratings, etc. The system performance measures are especially useful to determine how fast a particular system will be for an end user of canned applications. However, the system measures are not as useful for predicting performance of applications developed by the user. Most of the popular benchmarks tend to gloss over the joint impact of the operating system and compilation tools on execution time. Additional factors, such as the particular mix of computations, system hardware and compilation settings also impact end performance and are not isolated by most of the popular measures.
Therefore, our goal is to look at the impact of the operating system (and compilation tools) on computational performance. By isolating these factors, we can consider if Linux “measures up” to the performance of commercial Windows environments.
We tested the performance of a reasonably intensive C program which we developed and use in-house. (This program can be downloaded from ftp.linuxjournal.com/pub/lj/listings/issue67/3425.tgz.) The program implements a volume visualization technique on a medical data set. This application has moderate computational and memory requirements—the data set used in our tests is approximately 4.5MB in size and requires in excess of 300,000,000 arithmetic operations in C (most of which are floating-point calculations) to compute the visualization. (See Figure 1.) An application of this type is a reasonable test of the computational performance enabled by the operating systems and compilation tools.
The visualization is static (no animation is involved), so we'll consider only the time to compute the visualization. The time to display the final image (see Figure 2) doesn't vary much between machines and invariably depends somewhat on the difference in graphics hardware, which is not one of the factors we wanted to consider. We also do not consider data input and output times; our goal is to see how efficient the operating systems and compilers are for computations.
As much as possible, we wanted to concentrate in isolation on factors that would allow us to determine which combination of operating system and compilation tools produced the best performance. Luckily, our lab has a few dual-boot machines (i.e., multiple operating systems are installed on these machines and the desired OS can be selected upon boot-up). These machines are particularly useful for performance testing, because the same hardware is used for both Windows and Linux. However, to provide a larger set of data, we also looked at performance on several single-boot PCs.
During our tests, we ensured that no other non-OS tasks were running on the computers. Although there is some evidence that UNIX in general may exhibit more graceful degradation in performance under increasing system load than Windows 95/98, it is challenging to duplicate comparable loads across different machines, so we'll concentrate mostly on unloaded performance. The tests were conducted immediately following system reboot and the average of the first three runs immediately following reboot are reported. Computation time was determined using the standard C clock function which returns process CPU time (at least under Linux—later, we'll discuss a bug in the clock of many Windows environments). To ensure the most optimistic measure of time, we've launched the application using several mechanisms and reported only the best time. Sometimes an application under Windows runs fastest within a compilation development environment, but in other cases, an application will run fastest directly from a command-line prompt. We report whichever produced the fastest execution time.
The following computers were utilized for the tests:
PC 1: Pentium II/233MHz with 96 MB 66MHz SDRAM, 4.3GB Ultra DMA hard disk and dual-bootable to Windows NT or Red Hat Linux 5.2.
PC 2: Pentium II/400MHz with 128MB 100MHz SDRAM, 9GB Ultra DMA hard disk and dual-bootable to Windows NT or Red Hat Linux 5.2.
PC 3: AMD K6-2/300MHz with 64MB 100MHz SDRAM, 4.3GB Ultra DMA hard disk and dual-bootable to Windows 95 OSR 2 (with Ultra DMA disk drivers) or Red Hat Linux 5.2.
PC 4: Pentium II/350MHz with 128MB 100MHz SDRAM, 6.4GB Ultra DMA hard disk and Windows NT.
PC 5: Pentium II/350MHz with 128MB 100MHz SDRAM, 6.4GB Ultra DMA hard disk and Windows 98.
PC 6: Pentium II/450MHz with 128MB 100MHz SDRAM, 6.4GB Ultra DMA hard disk and Windows 98.
PC 7: Pentium II/400MHz with 256MB 100MHz SDRAM, 2 x 8GB Ultra DMA hard disks and Red Hat Linux 5.1.
PC 8: Pentium II/350MHz with 64MB 100MHz SDRAM, 4.3GB Ultra DMA hard disk and Windows NT.
PC 9: Pentium II/400MHz, identical hardware to PC 2 but bootable only to Windows NT.
- Machine Learning Everywhere
- Own Your DNS Data
- Bash Shell Script: Building a Better March Madness Bracket
- Understanding OpenStack's Success
- Simple Server Hardening
- Returning Values from Bash Functions
- Understanding Firewalld in Multi-Zone Configurations
- From vs. to + for Microsoft and Linux
- Natalie Rusk's Scratch Coding Cards (No Starch Press)
- Tech Tip: Really Simple HTTP Server with Python