Probably 90 percent of all the processing I do is with datasets that are in the two to four million record range and perhaps 5% are in the eight million record range and as big as 4GB in size. The datasets that have more than eight million records really don’t make use of the cache and run slightly (and insignificantly) slower than if there was no cache whatsoever.įor the most part I am satisfied with the improvement afforded by Fanc圜ache.
#Fancycache trial#
I suspect that the fourth trial which has eight million records is getting flushed meaning some of the data is being written to the hard drive and staying in the cache. It appears that all the data for the first three trials are running directly out of the cache. I’m pretty sure at this point that I am CPU bound. By looking at the chart above, we also see that Real Time is lower than CPU time for the first four datasets that are between one million and eight million records. This is pretty breathtaking and shows just how slow a hard disk can be compared to reading and writing to memory.Īs they say, the proof is in the pudding and it’s time to look at the benchmarks with Fanc圜ache running on the workstation.īy simply using a disk cache and utilizing system RAM in a more efficient manner, we are able to reduce processing time for datasets of eight million records and less. the 4K IOPS for Read is a whopping 700 times faster and for write it is 260 times faster. Write speeds are an incredible 90 times faster. Read speed is 136.9 times faster with Fanc圜ache. Compare the above chart with the baseline chart we did in the previous post for the same drive (see below). Now this is amazing! The performance improvement is out of sight. Using the Anvil’s storage Utilities, lets see what kind of performance our D drive (used for WPS work space) is capable of. I also enabled Deferred Writes and set the latency to 10 seconds.Īfter you set the configuration, you will have to reboot your workstation for Fanc圜ache to utilize your new settings. I’ve tested Fanc圜ache using different block sizes and algorithms and I found that a block size of 4K with the LRU (least Recently Used) algorithm with 4GB of RAM works best for me.
#Fancycache download#
It is currently in beta test, so if you desire, you can download and give it a spin for 180 days to see if it is something that you are comfortable using.Īfter installing Fanc圜ache, you are provide with a screen that allows you to configure the software. For my testing and tuning, I’m going to use Fanc圜ache. I’ve found two utilities that implement caching and they are SuperCache by SuperSpeed and Fanc圜ache by Romex Software.
#Fancycache software#
First, we need to find some software that makes use of some of that memory for disk caching. With 16GB of RAM, I think we should try to make better use of that memory than by letting it just sit there waiting to be used by WPS or other processes. It’s obvious that this machine is being held back by I/O. So in this post, I’m going to step through some performance tuning and see what kind of performance gains we can realize. I expected a bit more “oomph” right out of the box, especially for the smaller size datasets. In the previous post, Analytic Workstations – Part I, I touched on my disappointment on the performance of a new workstation that I had just built.
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