I want to read performance monitoring couters in Core i7
The out put for each event just contains 1 data and has no information about the core this data is for.
How can I read the events counts for each core separately by PAPI?
THANK YOU SO MUCH
PAPI counts are based on threads and not on cores. If you want core-based measurements, you may want to consider using intel pcm - which is capable of providing per-core counts.
The PCM is bit tricky to use and the counts may not match with PAPI - since PCM counts or accounts things slightly differently.
Does it answer your question?
tjr
Related
I am new to rocket chip. I want to design a coprocessor to accelerate data processing. I have a question about how to do a large chunk of data exchange between core and accelerator for one custom instruction. I am wondering if dcache can be used for this data exchange. I went to check LazyRoCC.scala and found io.mem.resp.bits.data only xLen bits long. Does it mean the length of data exchange between dcache and RoCC is limited to xLen? Is there any other way to do this data exchange? Thank you in advance!
this question may come as being too broad, but I will try to make every sub-topic to be as specific as possible.
My setting:
Large binary input (2-4 KB per sample) (no images)
Large binary output of the same size
My target: Using Deep Learning to find a mapping function from my binary input to the binary output.
I have already generated a large training set (> 1'000'000 samples), and can easily generate more.
In my (admittedly limited) knowledge of Neural networks and deep learning, my plan was to build a network with 2000 or 4000 input nodes, the same number of output nodes and try different amounts of hidden layers.
Then train the network on my data set (waiting several weeks if necessary), and checking whether there is a correlation between in- and output.
Would it be better to input my binary data as single bits into the net, or as larger entities (like 16 bits at a time, etc)?
For bit-by-bit input:
I have tried "Neural Designer", but the software crashes when I try to load my data set (even on small ones with 6 rows), and I had to edit the project save files to set Input and Target properties. And then it crashes again.
I have tried OpenNN, but it tries to allocate a matrix of size (hidden_layers * input nodes) ^ 2, which, of course, fails (sorry, no 117GB of RAM available).
Is there a suitable open-source framework available for this kind of
binary mapping function regression? Do I have to implement my own?
Is Deep learning the right approach?
Has anyone experience with these kind of tasks?
Sadly, I could not find any papers on deep learning + binary mapping.
I will gladly add further information, if requested.
Thank you for providing guidance to a noob.
You have a dataset containing pairs of binary valued vectors, with a max length of 4,000 bits. You want to create a mapping function between the pairs. On the surface, that doesn't seem unreasonable - imagine a 64x64 image with binary pixels – this only contains 4,096 bits of data and is well within the reach of modern neural networks.
As your dealing with binary values, then a multi-layered Restricted Boltzmann Machine would seem like a good choice. How many layers you add to the network really depends on the level of abstraction in the data.
You don’t mention the source of the data, but I assume you expect there to be a decent correlation. Assuming the location of each bit is arbitrary and is independent of its near neighbours, I would rule out a convolutional neural network.
A good open source framework to experiment with is Torch - a scientific computing framework with wide support for machine learning algorithms. It has the added benefit of utilising your GPU to speed up processing thanks to its CUDA implementation. This would hopefully avoid you waiting several weeks for a result.
If you provide more background, then maybe we can home in on a solution…
I am looking for a convenient way to store and to query huge amount of meteorological data (few TB). More information about the type of data in the middle of the question.
Previously I was looking in the direction of MongoDB (I was using it for many of my own previous projects and feel comfortable dealing with it), but recently I found out about HDF5 data format. Reading about it, I found some similarities with Mongo:
HDF5 simplifies the file structure to include only two major types of
object: Datasets, which are multidimensional arrays of a homogenous
type Groups, which are container structures which can hold datasets
and other groups This results in a truly hierarchical, filesystem-like
data format. Metadata is stored in the form of user-defined, named
attributes attached to groups and datasets.
Which looks like arrays and embedded objects in Mongo and also it supports indices for querying the data.
Because it uses B-trees to index table objects, HDF5 works well for
time series data such as stock price series, network monitoring data,
and 3D meteorological data.
The data:
Specific region is divided into smaller squares. On the intersection of each one of the the sensor is located (a dot).
This sensor collects the following information every X minutes:
solar luminosity
wind location and speed
humidity
and so on (this information is mostly the same, sometimes a sensor does not collect all the information)
It also collects this for different height (0m, 10m, 25m). Not always the height will be the same. Also each sensor has some sort of metainformation:
name
lat, lng
is it in water, and many others
Giving this, I do not expect the size of one element to be bigger than 1Mb.
Also I have enough storage at one place to save all the data (so as far as I understood no sharding is required)
Operations with the data.
There are several ways I am going to interact with a data:
convert as store big amount of it: Few TB of data will be given to me as some point of time in netcdf format and I will need to store them (and it is relatively easy to convert it HDF5). Then, periodically smaller parts of data (1 Gb per week) will be provided and I have to add them to the storage. Just to highlight: I have enough storage to save all this data on one machine.
query the data. Often there is a need to query the data in a real-time. The most of often queries are: tell me the temperature of sensors from the specific region for a specific time, show me the data from a specific sensor for specific time, show me the wind for some region for a given time-range. Aggregated queries (what is the average temperature over the last two months) are highly unlikely. Here I think that Mongo is nicely suitable, but hdf5+pytables is an alternative.
perform some statistical analysis. Currently I do not know what exactly it would be, but I know that this should not be in a real time. So I was thinking that using hadoop with mongo might be a nice idea but hdf5 with R is a reasonable alternative.
I know that the questions about better approach are not encouraged, but I am looking for an advice of experienced users. If you have any questions, I would be glad to answer them and will appreciate your help.
P.S I reviewed some interesting discussions, similar to mine: hdf-forum, searching in hdf5, storing meteorological data
It's a difficult question and I am not sure if I can give a definite answer but I have experience with both HDF5/pyTables and some NoSQL databases.
Here are some thoughts.
HDF5 per se has no notion of index. It's only a hierarchical storage format that is well suited for multidimensional numeric data. It's possible to extend on top of HDF5 to implement an index (i.e. PyTables, HDF5 FastQuery) for the data.
HDF5 (unless you are using the MPI version) does not support concurrent write access (read access is possible).
HDF5 supports compression filters which can - unlike popular belief - make data access actually faster (however you have to think about proper chunk size which depends on the way you access the data).
HDF5 is no database. MongoDB has ACID properties, HDF5 doesn't (might be important).
There is a package (SciHadoop) that combines Hadoop and HDF5.
HDF5 makes it relatively easy to do out core computation (i.e. if the data is too big to fit into memory).
PyTables supports some fast "in kernel" computations directly in HDF5 using numexpr
I think your data generally is a good fit for storing in HDF5. You can also do statistical analysis either in R or via Numpy/Scipy.
But you can also think about a hybdrid aproach. Store the raw bulk data in HDF5 and use MongoDB for the meta-data or for caching specific values that are often used.
You can try SciDB if loading NetCDF/HDF5 into this array database is not a problem for you. Note that if your dataset is extremely large, the data loading phase will be very time consuming. I'm afraid this is a problem for all the databases. Anyway, SciDB also provides an R package, which should be able to support the analysis you need.
Alternatively, if you want to perform queries without transforming HDF5 into something else, you can use the product here: http://www.cse.ohio-state.edu/~wayi/papers/HDF5_SQL.pdf
Moreover, if you want to perform a selection query efficiently, you should use index; if you want to perform aggregation query in real time (in seconds), you can consider approximate aggregation. Our group has developed some products to support those functions.
In terms of statistical analysis, I think the answer depends on the complexity of your analysis. If all you need is to compute something like entropy or correlation coefficient, we have products to do it in real time. If the analysis is very complex and ad-hoc, you may consider SciHadoop or SciMATE, which can process scientific data in the MapReduce framework. However, I am not sure if SciHadoop currently can support HDF5 directly.
I've been tasked with writing a program that does streaming sums of vectors into scattered memory locations, at the absolute max speed possible. The input data is a destination ID and an XYZ float vectors, so something like:
[198, {0.4,0,1}], [775, {0.25,0.8,0}], [12, {0.5,0.5,0.02}]
and I need to sum them into memory like so:
memory[198] += {0.4,0,1}
memory[775] += {0.25,0.8,0}
memory[12] += {0.5,0.5,0.02}
To complicate matters, there will be multiple threads doing this at the same time, reading from different input streams but summing to the same memory. I don't anticipate there being a lot of contention for the same memory locations, but there will be some. The data sets will be pretty large - multiple streams of 10+ GB apiece that we'll be streaming simultaneously from multiple SSDs to get the highest possible read bandwidth. I'm assuming SSE for the math, although it certainly doesn't have to be that way.
The results won't be used for a while, so I don't need to pollute the cache... but I'm summing into memory, not just writing, so I can't use something like MOVNTPS, right? But since the threads won't be stepping on each other that much, how can I do this without a lot of locking overhead? Would you do this with memory fencing?
Thanks for any help. I can assume Nehalem and above, if that makes a difference.
You can use spin locks for synchronized access to array elements (one per ID) and SSE for summing. In C++, depending on the compiler, intrinsic functions may be available, e.g. Streaming SIMD Extensions and InterlockExchange in Visual C++.
Your program's performance will be limited by memory bandwidth. Don't expect significant speed improvement from multithreading unless you have a multi-CPU (not just multi-core) system.
Start one thread per CPU. Statically distribute destination data between these threads. And provide each thread with the same input data. This allows better use of NUMA architecture. And avoids extra memory traffic for thread synchronization.
In case of single-CPU system, use only one thread accessing destination data.
Probably, the only practical use for more cores in CPUs is to load input data with additional threads.
One obvious optimization is to align destination data by 16 bytes (to avoid touching two cache lines while accessing single data element).
You can use SIMD to perform the addition, or allow compiler to automatically vectorize your code, or just leave this operation completely unoptimized - it doesn't matter, it's nothing compared to the memory bandwidth problems.
As for polluting the cache with output data, MOVNTPS cannot help here, but you can use PREFETCHNTA to prefetch output data elements several steps ahead while minimizing cache pollution. Will it improve performance or degrade it, I don't know. It avoids cache trashing, but leaves most of the cache unused.
Considering that a processor runs at 100 MHz and the data is coming to the processor from an external device/peripheral at the rate of 1000 Mbit/s (8 Bits/Clockcycle # 125 MHz), which is the best way to handle traffic that comes at a higher speed to the processor ?
First off, you can't do it in software. There would be no way to sample the digital lines at a sufficient rate, or to doing anything useful with it.
You need to use a hardware FIFO buffer or memory cell. When a data burst comes in, it can be buffered in the high speed FIFO and then read out as needed by the processor.
Drop in high speed FIFO chips are surprisingly expensive (though most are dual ported). To cut cost, you would be best off using an SRAM chip, and a hardware adder to increment the address lines on incoming data.
This is not an uncommon situation for software. semaj said the right word. This is a system engineering issue. Other folks have the right answer too. If you want to look at or process that data with the 100MHz processor, it is not going to happen, dont bother trying. You CAN look at snapshots of it or have the hardware filter out a specific percentage of it that you are looking for. At the end of the day though it is a systems issue, what does the hardware provide, where does it put this data, what is the softwares task for this data, does it see X buffers of data come in on the goesinta, and the notify the goesouta hardware that there are X buffers ready to go? Does the hardware examine and align the buffers so that you can look at a header, and then decide where to route the hardware? Once you do your system engineering you will know if you can use that processor or not, and if you can use it what its job is and how to do it.
Your direct question. What is the best way to handle it. The best way to handle it is to have hardware (fpga, asic, etc) move it into and out of some storage device (ram of some sort probably). Not necessarily the same ram the processor runs out of (DMA is a good thing to avoid). The hardware is something the software can talk to but you cannot examine all of that data so dont try. Without knowing what kind of data this is, what form, what the software looks at how much work you are willing to force the hardware to do, etc determines the rest of the answer. If you expect a certain (guaranteed) percentage to be bad or not belong to this processor, etc have the hardware filter that out and then what is left you can process.
Networking is a good example of this, PCs have gige ports but cannot process GigE line rate data. That is why we use switches now instead of hubs, hardware slices out a percentage of the data so the pc can handle it, the protocols take care of the data that cannot be processed by resending it later. And the switches processors dont look at all of the data, the hardware slices it up so the software can examine just the header. Or sometimes the software simply manages tables that drive the hardware and the hardware does all the work of processing the data.
Do your system engineering the answers will simply fall out.
You buffer it. Typically data from a device is written to a memory buffer (circular queue) using DMA (no cpu involved). The cpu reads from the memory buffer at a constant rate. Usually devices send data in bursts. This keeps the buffer from filling up. If there is too much data, buffer overflow.
DMA (direct memory access) is possibly the solution, however, it seems unlikely that the memory bus could run faster than the processor core, so the receiving peripheral would have to accept data into a larger register than 8 bit because 125MHz could not be sustained. For example a 16bit register would allow memory writes at 62.5MHz which may be achievable. Also the receiving device would have to be able to accept an external clock that is both faster and asynchronous to the core clock. Also of course the receiving peripheral must have support for DMA.
Unless you are more specific about your hardware and the communication protocol it is difficult to give anything other than a general answer.