Tag Archives: large

Do large intraday reversals always signal a continuation in the latter direction in the short term/long term?

Do large intraday reversals always signal a continuation in the latter direction in the short term/long term?
–I don’t have any statistics related to your question, but I know that many intraday reversals are only intraday traders inventory adjustments. –Many such intraday reversals are inventory adjustment as as Michele replied or trapped traders squaring off. Not all intraday reversals fall into this category. Watch the price action next day, if it revisits the prior day reversal levels, then it is likely to be inventory adjustment.
–Any Trend Reversal will begin with an Intraday Reversal but all Intraday Reversals will not be Trend Reversals. I dont have the Statistics but can tell you the probability will be very low. But if you are aware of Crucial Levels and you see a Reversal at those levels you can catch the down Move Early.

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Quant analytics: Is GPU good for large vector addition?

Quant analytics: Is GPU good for large vector addition?

No. CPU is about 4 times faster for very large vector addition. Here is my experiment. http://lnkd.in/bTzxge

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Is a GPU good for large vector addition?
Yes! A GPU is much faster than a CPU, since we talk about large vectors here.

The assumption you have made is that input and output data should reside in ‘CPU’ memory rather than in ‘GPU’ memory. IMO it is better to decouple the time required to copy data. So we talk about the same, but draw a different conclusion.

For more timings on CPU and GPU have a look at www.tivipe.com

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And just how is a 4 times improvement not good?

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Your results are probably correct, but meaningless: no sane use case could comprise only of using GPU to add vectors, so SAXPY would be certainly just one of the operations in the sequence, and relevant comparison would be, as Tino mentioned above, with host/device memory transfer timings taken out of the picture. Besides, there was really not much need to write a paper on the whole issue: almost all GPU programming guides are already mentioning (oftentimes pointing exactly to SAXPY as an example of an operation that is not suitable in that regard) that in order to utilize GPU to the full speed, one would need high ratio of arithmetic operations vs. memory accesses.

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vector addition is always presented as a naive example as it is very easily understood. As with current memory transfer overheads CPU are better at it. But the situation would be very interesting in cases of APUs where transfer bandwidth from RAM to CPU and GPU would be comparable.

Have you done some investigations in this regard.

 

 

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Better alternatives to TFTP for network booting on large clusters??…

Better alternatives to TFTP for network booting on large clusters??…

 

I have used the slam protocol that is based on multicast. It is kind of dead project but you can grab server and client from the etherboot/gpxe code. If you are willing to self support, then it works great.

 

 

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Need real world example of large GWT-platform with GWT MVP and GWT 2? Download here

Need real world example of large GWT-platform with GWT MVP and GWT 2? Download here

Get it here:

http://code.google.com/p/puzzlebazar/source/checkout

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Matlab`s parallel computing toolkits enables you batch and breakup large data set processing

Matlab`s parallel computing toolkits enables you batch and breakup large data set processing
We all know about parallel computing capabilities. C++ is quite powerful at doing it but you need to hand code everything to take advantage of it. It seems Matlab includes a parallel computing toolkit which makes life easier.
You can break up the complicated and long running processes into small worker group sets. This allows you to run Matlab more efficiently and your tasks will take a shorter amount of time to complete. Matlab enables you run as many remote workers as you can based on the license you get from Mathworks. Out of the box, it seems Matlab limits you to eight workers running in parallel.
Some uses for this parallel computing feature is when you have long iterations running or you have too many running. You could use the parfor function instead of the standard for loop to take full advantage of the parallel computing capabilities. You could also batch jobs or shorten long data sets using this Matlab parallel computing toolkit.
With the parallel computing toolkit, you can get:
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Different Array Types
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Working with Co distributed Arrays
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Using a for-Loop Over a Distributed Range (for-drange)
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Using MATLAB Functions on Codistributed Arrays
Thankfully, Matlab`s help files give you a run down on this very powerful feature which many application cannot offer. I am very, very impressed with this capability which includes the capability to run a profiler that simultaneously displays each worker. It also has the ability to talk to most scheduling software programs which makes Matlab`s parallel computing features even better. Wow! You need to check out some of the powerful function calls to be make this happen.
Also, this parallel computing toolkits makes Matlab a very modern architecture as you may want to consider using instead of pure C++ home build applications.

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