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This is a short tutorial for the basic usage of IOR and some tips on how to useIOR to handle caching effects as these are very likely to affect yourmeasurements.
Running IOR¶
There are two ways of running IOR:
- Command line with arguments – executable followed by command line
- options.
- ::
- $ ./IOR -w -r -o filename Docs for mac os download.
This performs a write and a read to the file ‘filename’.
- Command line with scripts – any arguments on the command line will
- establish the default for the test run, but a script may be used inconjunction with this for varying specific tests during an execution ofthe code. Only arguments before the script will be used!
- ::
- $ ./IOR -W -f script
This defaults all tests in ‘script’ to use write data checking.
Freeagent goflex drive not responding. In this tutorial the first one is used as it is much easier to toy around withan get to know IOR. The second option thought is much more useful to safebenchmark setups to rerun later or to test many different cases.
Getting Started with IOR¶
IOR writes data sequentially with the following parameters:
- blockSize (-b)
- transferSize (-t)
- segmentCount (-s)
- numTasks (-n)
which are best illustrated with a diagram:
Playstation 3 controller driver for windows 8. These four parameters are all you need to get started with IOR. However,naively running IOR usually gives disappointing results. For example, if we runa four-node IOR test that writes a total of 16 GiB:
we can only get a couple hundred megabytes per second out of a Lustre filesystem that should be capable of a lot more.
Switching from writing to a single-shared file to one file per process using the-F (filePerProcess=1) option changes the performance dramatically: Download video from sony handycam to mac.
This is in large part because letting each MPI process work on its own file cutsout any contention that would arise because of file locking.
However, the performance difference between our naive test and thefile-per-process test is a bit extreme. In fact, the only way that 146 GB/secread rate could be achievable on Lustre is if each of the four compute nodes hadover 45 GB/sec of network bandwidth to Lustre–that is, a 400 Gbit link on everycompute and storage node.
Effect of Page Cache on Benchmarking¶
What’s really happening is that the data being read by IOR isn’t actually comingfrom Lustre; rather, files’ contents are already cached, and IOR is able toread them directly out of each compute node’s DRAM. The data wound up gettingcached during the write phase of IOR as a result of Linux (and Lustre) using awrite-back cache to buffer I/O, so that instead of IOR writing and reading datadirectly to Lustre, it’s actually mostly talking to the memory on each computenode.
To be more specific, although each IOR process thinks it is writing to a file onLustre and then reading back the contents of that file from Lustre, it isactually
- writing data to a copy of the file that is cached in memory. If thereis no copy of the file cached in memory before this write, the partsbeing modified are loaded into memory first.
- those parts of the file in memory (called “pages”) that are nowdifferent from what’s on Lustre are marked as being “dirty”
- the write() call completes and IOR continues on, even though the writtendata still hasn’t been committed to Lustre
- independent of IOR, the OS kernel continually scans the file cache forfiles who have been updated in memory but not on Lustre (“dirt pages”),and then commits the cached modifications to Lustre
- dirty pages are declared non-dirty since they are now in sync withwhat’s on disk, but they remain in memory
Then when the read phase of IOR follows the write phase, IOR is able to justretrieve the file’s contents from memory instead of having to communicate withLustre over the network.
There are a couple of ways to measure the read performance of the underlyingLustre file system. The most crude way is to simply write more data than willfit into the total page cache so that by the time the write phase has completed,the beginning of the file has already been evicted from cache. For example,increasing the number of segments (-s) to write more data reveals the point atwhich the nodes’ page cache on my test system runs over very clearly:
However, this can make running IOR on systems with a lot of on-node memory takeforever.
A better option would be to get the MPI processes on each node to only read datathat they didn’t write. For example, on a four-process-per-node test, shiftingthe mapping of MPI processes to blocks by four makes each node N read the datawritten by node N-1.
Since page cache is not shared between compute nodes, shifting tasks this wayensures that each MPI process is reading data it did not write.
IOR provides the -C option (reorderTasks) to do this, and it forces each MPIprocess to read the data written by its neighboring node. Running IOR withthis option gives much more credible read performance:
But now it should seem obvious that the write performance is also ridiculouslyhigh. And again, this is due to the page cache, which signals to IOR that writesare complete when they have been committed to memory rather than the underlyingLustre file system.
To work around the effects of the page cache on write performance, we can issuean fsync() call immediately after all of the write()s return to force the dirtypages we just wrote to flush out to Lustre. Including the time it takes forfsync() to finish gives us a measure of how long it takes for our data to writeto the page cache and for the page cache to write back to Lustre.
IOR provides another convenient option, -e (fsync), to do just this. And, onceagain, using this option changes our performance measurement quite a bit:
and we finally have a believable bandwidth measurement for our file system.
Defeating Page Cache¶
Since IOR is specifically designed to benchmark I/O, it provides these optionsthat make it as easy as possible to ensure that you are actually measuring theperformance of your file system and not your compute nodes’ memory. That beingsaid, the I/O patterns it generates are designed to demonstrate peak performance,not reflect what a real application might be trying to do, and as a result,there are plenty of cases where measuring I/O performance with IOR is not alwaysthe best choice. There are several ways in which we can get clever and defeatpage cache in a more general sense to get meaningful performance numbers.
When measuring write performance, bypassing page cache is actually quite simple;opening a file with the O_DIRECT flag going directly to disk. Hide ip free download mac. In addition,the fsync() call can be inserted into applications, as is done with IOR’s -eoption.
Measuring read performance is a lot trickier. If you are fortunate enough tohave root access on a test system, you can force the Linux kernel to empty outits page cache by doing
- ::
- # echo 1 > /proc/sys/vm/drop_caches
and in fact, this is often good practice before running any benchmark(e.g., Linpack) because it ensures that you aren’t losing performance to thekernel trying to evict pages as your benchmark application starts allocatingmemory for its own use.
Unfortunately, many of us do not have root on our systems, so we have to geteven more clever. As it turns out, there is a way to pass a hint to the kernelthat a file is no longer needed in page cache:
The effect of passing POSIX_FADV_DONTNEED using posix_fadvise() is usually thatall pages belonging to that file are evicted from page cache in Linux. However,this is just a hint–not a guarantee–and the kernel evicts these pagesasynchronously, so it may take a second or two for pages to actually leave pagecache. Fortunately, Linux also provides a way to probe pages in a file to seeif they are resident in memory.
Finally, it’s often easiest to just limit the amount of memory available forpage cache. Because application memory always takes precedence over cachememory, simply allocating most of the memory on a node will force most of thecached pages to be evicted. Newer versions of IOR provide the memoryPerNodeoption that do just that, and the effects are what one would expect:
The above diagram shows the measured bandwidth from a single node with 128 GiBof total DRAM. The first percent on each x-label is the amount of this 128 GiBthat was reserved by the benchmark as application memory, and the second percentis the total write volume. For example, the “50%/150%” data points correspondto 50% of the node memory (64 GiB) being allocated for the application, and atotal of 192 GiB of data being read.
This benchmark was run on a single spinning disk which is not capable of morethan 130 MB/sec, so the conditions that showed performance higher than this werebenefiting from some pages being served from cache. And this makes perfectsense given that the anomalously high performance measurements were obtainedwhen there was plenty of memory to cache relative to the amount of data beingread.
Corollary¶
Ior Tutorial Download For Mac Windows 10
Measuring I/O performance is a bit trickier than CPU performance in large partdue to the effects of page caching. That being said, page cache exists for areason, and there are many cases where an application’s I/O performance reallyis best represented by a benchmark that heavily utilizes cache.
For example, the BLAST bioinformatics application re-reads all of its input datatwice; the first time initializes data structures, and the second time fillsthem up. Because the first read caches each page and allows the second read tocome out of cache rather than the file system, running this I/O pattern withpage cache disabled causes it to be about 2x slower:
Ior Tutorial Download For Mac Download
Thus, letting the page cache do its thing is often the most realistic way tobenchmark with realistic application I/O patterns. Once you know how page cachemight be affecting your measurements, you stand a good chance of being able toreason about what the most meaningful performance metrics are.