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Closed 10 years ago.
I know commonly CPU has many compute units or CUDA cores. This make it suitable for compute-intensive algorithms.
But why it has so much more cores than CPU? When rending image, which kinds of algorithms are parallelizable?
This huge number of compute units is necessary for fast processing of frames when applying shaders.
This type of computing is highly parallelizable as each shader will be applied n times (maybe one time by pixel) and often in an independent way on the same frame.
Note that each compute-unit is made of many shader-cores.
This is why shaders support is a prerequisite for OpenCL as it implies some dedicated cores to do the rendering job, cores that can be "hijacked" to do other things => this is called GPGPU.
Related
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Closed 9 years ago.
I am looking for some suggestions on using R for analysing Big Data - i.e., data that runs into TBs.
Typically I think that it is better to preprocess the data and load just the information that the user needs in order to perform analysis. However, if say information from a large dataset (say, 200 GB) needs to be aggregated, I think first, having the data stored in column databases rather than row-oriented DBMS would be more efficient. Second, for CPU intensive data analysis, it is probably worthwhile having some distributed computing capabilities using RHadoop / RHIPE. Also, if there are multiple enterprise users, what would be the best way to implement these ... (say 10 researchers who are concurrently working on large datasets)
I have found some resources on the web such as the R indexing, mmap packages to do efficient computations in R but wanted to get some feedback from those who have actually worked and implemented these at an enterprise level.
Thanks in advance for your suggestions,
Regards.
As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance.
Closed 10 years ago.
I am using shared memory (shmget system calls provided by C) to allocate shared memory from inside a RCpp program.
Here as i am not using standard Calloc function which is connected with memory management of R , is there any danger i should be aware of ?
Will the memory management of R sees the space allocated by shmget as free space and tries to over write anything to it ?
If that is the case , what can be done to avert this situation ?
Thanks
Vineeth
This shows a complete lack of understanding of R.
You can neither make R multithreaded just by linking to pthreads, or using OpenMP, or ... By the same token, "Writing R Extensions" is very clear about what can and cannot be done with memory allocation. I suggest you take a closer look at that manual.
As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance.
Closed 10 years ago.
With the rise of Scala.React I was wondering whether Qt's Signals & Slots mechanism would become obsolete when using Qt as a GUI framework for a Scala program. How would one of the two approaches excel in each of the following categories?
ease of coding, regarding conciseness and clarity
expressiveness: Does any technique provide possibilities that the other one does not (like with WPF's coerce mechanism of dependency properties)?
compile time type safety, e.g. when using QtScript to define Signals & Slots
performance - But would it actually matter in a GUI?
Suppose Scala.React was already in a completed state and well documented: When would you prefer one approach over the other?
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Closed 11 years ago.
There are a lot of opensources build systems, and most of them are multiplatform, multilanguage, cross-everything.
I'm not interested on which one is the best (which would be offtopic by the way), but I'd like to know which ones are used most.
So, do we have any kind of statistic about their actual usage? Could you provide any link?
As there is no tracking process, I doubt that this kind of information is available. You could check the number of artifacts in maven central for example, but it won't tell that much about actual usage and may be viewed rather as a trend. Some build tool developers list some of the most reknowned users on their sites, most don't
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Closed 11 years ago.
I am looking for a good platform to rewrite a growing website with huge amount of daily and concurrent visitors! so the Required Parameters are:
Render Time With minimum CPU and Memory Usage
Response Time
Extendability in Architecture (Best support for new features)
Maintainability
Security Capabilities
Minimum Hardware usage
I know Spring, GORM, but they will slow it down! also I did not mentioned ASP.net MVC because of Tradeoff between Speed and MVC Pattern Benefits!
I know that all of these parameters should be considered as a tradeoff so then I can choose my best fitting platform! Can some body provide a good and reliable comparison between GRails and ASP.net in Items provided above ?