Real-Time Data Compression's Impact on SSD Throughput Capability Page 2 -

Real-Time Data Compression's Impact on SSD Throughput Capability Page 2

Testing/Benchmarking Approach and Setup

The old phrase of "if you're going to do it, do it right," definitely rings true for benchmarking. All too often storage benchmarks are nothing less than marketing materials providing very little useful information. In this article, I will follow concepts that aim to improve the quality of the benchmarks. In particular:

  • The motivation behind the benchmarks will be explained (if it hasn't already).
  • Relevant and useful storage benchmarks will be used.
  • The benchmarks will be detailed as much as possible.
  • The tests will run for more than 60 seconds.
  • Each test is run 10 times, and the average and standard deviation of the results will be reported.

These basic steps and techniques can make benchmarking and testing much more useful.

The benchmarks in this article are designed to explore the write, read, random write, random read, and fread, fwrite performance of the SSD. I don't really know what the results will be prior to running the tests so there are no expectations -- it's really an exploration of the performance.

This article examines the performance of a 64GB Micro Center SSD that uses a SandForce 1222 controller. This is a very inexpensive drive that provides about 60GB of useable space for just under $100. ($1.67/GB). The specifications on the website state the drive has a SATA 3.0 Gbps interface (SATA II), and it has a performance of up to 270 MB/s for writes, 280 MB/s for reads, and up to 50,000 IOPS.

The highlights of the system used in the testing are:

  • GigaByte MAA78GM-US2H motherboard
  • An AMD Phenom II X4 920 CPU
  • 8GB of memory (DDR2-800)
  • Linux 2.6.32 kernel
  • The OS and boot drive are on an IBM DTLA-307020 (20GB drive at Ultra ATA/100)
  • /home is on a Seagate ST1360827AS
  • The Micro Center SSD is mounted as /dev/sdd

I used CentOS 5.4 on this system, but I used my own kernel -- 2.6.32. Ext4 will be used as the file system as well. The entire device, /dev/sdd was used for the file system so it is aligned with page boundaries.

I used IOzone because it is one of the most popular throughput benchmarks. It is open source and written in very plain ANSI C (not an insult but a compliment), and perhaps more importantly, it tests different I/O patterns, which very few benchmarks actually do. It is capable of single-thread, multi-thread, and multi-client testing. The basic concept of IOzone is to break up a file of a given size into records. Records are written or read in some fashion until the file size is reached. Using this concept, IOzone has a number of tests that can be performed. In the interest of brevity, I limited the benchmarks to write, read, random write, random read, fwrite, and fread, all of which are described below.

  • Write: This is a fairly simple test that simulates writing to a new file. Because of the need to create new metadata for the file, many times the writing of a new file can be slower than rewriting to an existing file. The file is written using records of a specific length (either specified by the user or chosen automatically by IOzone) until the total file length has been reached.
  • Read: This test reads an existing file. It reads the entire file, one record at a time.
  • Random Read: This test reads a file with the accesses being made to random locations within the file. The reads are done in record units until the total reads are the size of the file. Many factors impact the performance of this test, including the OS cache(s), the number of disks and their configuration, disk seek latency and disk cache.
  • Random Write: The random write test measures the performance when writing a file with the accesses being made to random locations with the file. The file is opened to the total file size, and then the data is written in record sizes to random locations within the file.
  • fwrite: This test measures the performance of writing a file using a library function "fwrite()". It is a binary stream function (examine the man pages on your system to learn more). Equally important, the routine performs a buffered write operation. This buffer is in user space (i.e., not part of the system caches). This test is performed with a record length buffer being created in a user-space buffer and then written to the file. This is repeated until the entire file is created. This test is similar to the "write" test in that it creates a new file, possibly stressing the metadata performance.
  • fread: This is a test that uses the fread() library function to read a file. It opens a file and reads it in record lengths into a buffer that is in user space. This continues until the entire file is read.

Other options can be tested, but for this exploration only the previously mentioned tests will be examined.

For IOzone, the system specifications are fairly important since they affect the command-line options. In particular, the amount of system memory is important because this can have a large impact on the caching effects. If the problem sizes are small enough to fit into the system or file system cache (or at least partially), it can skew the results. Comparing the results of one system where the cache effects are fairly prominent to a system where cache effects are not conspicuous is comparing the proverbial apples to oranges. For example, if you run the same problem size on a system with 1GB of memory compared to a system with 8GB you will get much different results.

For this article, cache effects will be limited as much as possible. Cache effects can't be eliminated entirely without running extremely large problems and forcing the OS to virtually eliminate all caches. However, one of the best ways to minimize the cache effects is to make the file size much bigger than the main memory. For this article, the file size is chosen to be 16GB, which is twice the size of main memory. This is chosen arbitrarily based on experience and some urban legends floating around the Internet.

For this article, the total file size was fixed at 16GB and four record sizes were tested: (1) 1MB, (2) 4MB, (3) 8MB, and (4) 16MB. For a file size of 16GB that is (1) 16,000 records, (2) 4,000 records, (3) 2,000 records, (4) 1,000 records, respectively. Smaller record sizes took too long to run since the number of records would be very large so they are not used in this article.

The command line for the first record size (1MB) is,

./IOzone -Rb spreadsheet_output_1M.wks -s 16G -+w 98 -+y 98 -+C 98 -r 1M > output_1M.txt

The command line for the second record size (4MB) is,

./IOzone -Rb spreadsheet_output_4M.wks -s 16G -+w 98 -+y 98 -+C 98 -r 4M > output_4M.txt

The command line for the third record size (8MB) is,

./IOzone -Rb spreadsheet_output_8M.wks -s 16G -+w 98 -+y 98 -+C 98 -r 8M > output_8M.txt

The command line for the fourth record size (16MB) is,

./IOzone -Rb spreadsheet_output_16M.wks -s 16G -+w 98 -+y 98 -+C 98 -r 16M > output_16M.txt

The options, "-+w 98", "-+y 98", and "-+C 98" are options that control the dedupability (compressibility) of the data. IOZone uses the phrase dedupe to describe if the data is capable of being deduplicated. This is basically the same as compressed, so I will use the phrases interchangeably. The number "98" in the options means the data is 98 percent dedupable, hence very compressible. These three options allow me to control the compressibility of the data, so I can examine the impact of data compressibility on performance.

I tested three levels of data compressibility -- 98 percent (very compressible), 50 percent, and 2 percent (very incompressible). I wanted to get an idea of the range of performance with these levels of data compressibility but I didn't want to get unrealistic results with either 100 percent compressible data or 0 percent compressible. (Though I'm not really sure if either of those are really possible.)

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