Bill Davidsen wrote:
quoted text
> Chris Snook wrote:
>> Bill Davidsen wrote:
>>> Chris Snook wrote:
>>>> Emmanuel Florac wrote:
>>>>> I post there because I couldn't find any information about this
>>>>> elsewhere : on the same hardware ( Athlon X2 3500+, 512MB RAM, 
>>>>> 2x400 GB
>>>>> Hitachi SATA2 hard drives ) the 2.4 Linux software RAID-1 (tested 
>>>>> 2.4.32
>>>>> and 2.4.36.2, slightly patched to recognize the hardware :p) is way
>>>>> faster than 2.6 ( tested 2.6.17.13, 2.6.18.8, 2.6.22.16, 2.6.24.3)
>>>>> especially for writes. I actually made the test on several different
>>>>> machines (same hard drives though) and it remained consistent across
>>>>> the board, with /mountpoint a software RAID-1.
>>>>> Actually checking disk activity with iostat or vmstat shows clearly a
>>>>> cache effect much more pronounced on 2.4 (i.e. writing goes on much
>>>>> longer in the background) but it doesn't really account for the
>>>>> difference. I've also tested it thru NFS from another machine (Giga
>>>>> ethernet network):
>>>>>
>>>>> dd if=/dev/zero of=/mountpoint/testfile bs=1M count=1024
>>>>>
>>>>> kernel        2.4       2.6        2.4 thru NFS   2.6 thru NFS
>>>>>
>>>>> write        90 MB/s    65 MB/s      70 MB/s       45 MB/s
>>>>> read         90 MB/s    80 MB/s      75 MB/s       65 MB/s
>>>>>
>>>>> Duh. That's terrible. Does it mean I should stick to  (heavily
>>>>> patched...) 2.4 for my file servers or... ? :)
>>>>>
>>>>
>>>> It means you shouldn't use dd as a benchmark.
>>>>
>>> What do you use as a benchmark for writing large sequential files or 
>>> reading them, and why is it better than dd at modeling programs which 
>>> read or write in a similar fashion?
>>>
>>> Media programs often do data access in just this fashion, 
>>> multi-channel video capture, streaming video servers, and similar.
>>>
>>
>> dd uses unaligned stack-allocated buffers, and defaults to block sized 
>> I/O.  To call this inefficient is a gross understatement.  Modern 
>> applications which care about streaming I/O performance use large, 
>> aligned buffers which allow the kernel to efficiently optimize things, 
>> or they use direct I/O to do it themselves, or they make use of system 
>> calls like fadvise, madvise, splice, etc. that inform the kernel how 
>> they intend to use the data or pass the work off to the kernel 
>> completely.  dd is designed to be incredibly lightweight, so it works 
>> very well on a box with a 16 MHz CPU.  It was *not* designed to take 
>> advantage of the resources modern systems have available to enable 
>> scalability.
>>
> dd has been capable of doing direct io for years, so I assume it can 
> emulate that behavior if it is appropriate to do so, and the buffer size 
> can be set as needed. I'm less sure that large buffers are allocated on 
> the stack, but often the behavior of the application models is the small 
> buffered writes dd would do by default.
>> I suggest an application-oriented benchmark that resembles the 
>> application you'll actually be using.
> 
> And this is what I was saying earlier, there is a trend to blame the 
> benchmark when in fact the same benchmark runs well on 2.4. Rather than 
> replacing the application or benchmark, perhaps the *regression* could 
> be fixed in the kernel. With all the mods and queued i/o and everything, 
> the performance is still going down.
> 

2.6 has been designed to scale, and scale it does.  The cost is added 
overhead for naively designed applications, which dd is quite 
intentionally.  Simply enabling direct I/O in dd accomplishes nothing if 
the I/O patterns you're instructing it to perform are not optimized.  If 
I/O performance is important to you, you really need to optimize your 
application or tune your kernel for I/O performance.

If you have a performance-critical application that is designed in a 
manner such that a naive dd invocation is an accurate benchmark for it, 
you should file a bug with the developer of that application.

I've long since lost count of the number of times that I've seen 
optimizing for dd absolutely killed real application performance.

-- Chris
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