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From Pearu Peterson <pearu.peter...@quansight.com>
Subject Re: Using CUDA enabled pyarrow
Date Wed, 03 Oct 2018 20:44:22 GMT
Hi,
I can make the initial design document from the existing comments.
Do you have examples of some earlier design documents used for similar
purpose? Would shared google docs be OK?

Btw, I also figured out an answer to my original question, here is a
partial codelet for accessing the batch columns that I was missing:

cbuf = <CudaBuffer instance>
cbatch = pa.cuda.read_record_batch(cbuf, schema)
for col in cbatch:
    null_buf, data_buf = col.buffers()
    cdata_buf = CudaBuffer.from_buffer(data_buf)
    if null_buf is not None: ...
    ...

This is used in CudaNDArray that allows accessing the items from host, very
similar to DeviceNDArray of numba.cuda:
  https://github.com/Quansight/pygdf/blob/arrow-cuda/pygdf/cudaarray.py
(excuse the coding, its wip and experimental)

Best regards,
Pearu




On Wed, Oct 3, 2018 at 11:29 PM Wes McKinney <wesmckinn@gmail.com> wrote:

> What are the action items on this? Sounds like we need to start a
> design document. I'm afraid I don't have the bandwidth to champion GPU
> functionality at the moment but I will participate in design
> discussions and help break down complex tasks into more accessible
> JIRA issues.
>
> Thanks
> Wes
> On Fri, Sep 28, 2018 at 9:44 AM Wes McKinney <wesmckinn@gmail.com> wrote:
> >
> > Seems like there is a fair bit of work to do to specify APIs and
> > semantics. I suggest we create a Google document or something
> > collaborative where we can enumerate and discuss the issues we want to
> > resolve, and then make a list of the concrete development.
> >
> > The underlying problem IMHO in ARROW-2446 is that we do not have the
> > notion of device. An instance of CudaBuffer is only necessary so that
> > the appropriate virtual dtor can be invoked to release the memory. As
> > long as a buffer referencing it is aware of the underlying device,
> > then our code can dispatch to the correct code paths. At the moment we
> > can only really detect whether an arrow::Buffer* is a device buffer by
> > dynamic_cast, and then that is not reliable because we may be a slice
> > On Fri, Sep 28, 2018 at 7:17 AM Pearu Peterson
> > <pearu.peterson@quansight.com> wrote:
> > >
> > > Hi Wes,
> > >
> > > Yes, it makes sense.
> > >
> > > If I understand you correctly then defining a device abstraction would
> also
> > > bring Buffer and CudaBuffer under the same umbrella (that would be
> opposite
> > > approach to ARROW-2446, btw).
> > >
> > > This issue is also related to
> > >   https://github.com/dmlc/dlpack/blob/master/include/dlpack/dlpack.h
> > > that defines a specification for data locality (for ndarrays but the
> > > concept is the same for buffers).
> > >
> > > ARROW-2447 defines API that uses Buffer::cpu_data(), hence also
> > > Buffer::cuda_data(), Buffer::disk_data() etc.
> > >
> > > I would like to propose a more general model (no guarantees that it
> would
> > > make sense implementation-wise :) ):
> > > 0. CPU would be considered as any other device (this would be in line
> with
> > > dlpack). To name few devices: HOST, CUDA, DISK, FPGA, etc. and why not
> > > remote databases defined by URL.
> > > 1. A device is defined as a unit that has (i) a memory for holding
> data,
> > > and (ii) it may have a processor(s) for processing the data
> (computations).
> > > For instance, HOST device has RAM and CPU(s); a CUDA device has device
> > > memory and GPU(s); a DISK device has memory but no processing unit,
> etc.
> > > 2. Different devices can access other devices memory using the same API
> > > methods (say, Buffer.data()). For processing the data by a device (in
> case
> > > the device has a processor), the data is copied to device memory
> on-demand,
> > > unless the data is stored in the same device as the the processor. For
> > > instance, for processing the CUDA data with CPU, HOST device would
> need to
> > > copy CUDA device data to HOST memory (that works currently) and
> vice-versa
> > > (that works as well, e.g. using CudaHostBuffer). In another setup, CUDA
> > > device might need to use data from DISK: according to this proposal,
> the
> > > DISK data would be copied directly to CUDA device (bypassing HOST
> memory if
> > > technically possible).
> > > So, in short, the implementation has to check whether the processor
> and the
> > > memory are on the same device before processing the data, if not, the
> data
> > > is copied using the on-demand approach. By on-demand approach, I mean
> that
> > > the data references are passed around as a pair: (device id, device
> > > pointer).
> > > 3. All the above is controlled from a master device process. Usually,
> the
> > > master device would be HOST, but it does not have to be always so.
> > >
> > > PS: I realize that this discussion diverges from the original subject,
> feel
> > > free to rename the subject if needed.
> > >
> > > Best regards,
> > > Pearu
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > > On Fri, Sep 28, 2018 at 12:49 PM Wes McKinney <wesmckinn@gmail.com>
> wrote:
> > >
> > > > hi Pearu,
> > > >
> > > > Yes, I think it would be a good idea to develop some tools to make
> > > > interacting with device memory using the existing data structures
> work
> > > > seamlessly.
> > > >
> > > > This is all closely related to
> > > >
> > > > https://issues.apache.org/jira/browse/ARROW-2447
> > > >
> > > > I would say step 1 would be defining the device abstraction. Then we
> > > > can add methods or properties to the data structures in pyarrow to
> > > > show the location of the memory, whether CUDA or host RAM, etc. We
> > > > could also have a memory-mapped device for memory maps to be able to
> > > > communicate that data is on disk. We could then define virtual APIs
> > > > for host-side data access to ensure that memory is copied to the host
> > > > if needed (e.g. in the case of indexing into the values of an array)
> > > >
> > > > There are some small details around the handling of device in the
> case
> > > > of hierarchical memory references. So if we say `buffer->GetDevice()`
> > > > then even if it's a sliced buffer (which will be the case after using
> > > > any IPC reader APIs), it needs to return the right device. This means
> > > > that we probably need to define a SlicedBuffer type that delegates
> > > > GetDevice() calls to the parent buffer
> > > >
> > > > Let me know if what I'm saying makes sense. Kou and Antoine probably
> > > > have some thoughts about this also.
> > > >
> > > > - Wes
> > > > On Fri, Sep 28, 2018 at 5:34 AM Pearu Peterson
> > > > <pearu.peterson@quansight.com> wrote:
> > > > >
> > > > > Hi,
> > > > >
> > > > > Consider the following use case:
> > > > >
> > > > > schema = <pa.Schema instance>
> > > > > cbuf = <pa.cuda.CudaBuffer instance>
> > > > > cbatch = pa.cuda.read_record_batch(schema, cbuf)
> > > > >
> > > > > Note that cbatch is pa.RecordBatch instance where data pointers are
> > > > device
> > > > > pointers.
> > > > >
> > > > > for col in cbatch.columns:
> > > > >     # here col is, say, FloatArray, that data pointer is a device
> pointer
> > > > >     # as a result, accessing col data, say, taking a slice, leads
> to
> > > > > segfaults
> > > > >     print(col[0])
> > > > >
> > > > > The aim of this message would be establishing a user-friendly way
> to
> > > > > access, say, a slice of the device data so that only the requested
> data
> > > > is
> > > > > copied to host.
> > > > >
> > > > > Or more generally, should there be a CUDA specific RecordBatch that
> > > > > implements RecordBatch API that can be used from host?
> > > > >
> > > > > For instance, this would be similar to DeviceNDArray in numba that
> > > > > basically implements ndarray API for device data while the API can
> be
> > > > used
> > > > > from host.
> > > > >
> > > > > What do you think? What would be the proper approach? (I can do the
> > > > > implementation).
> > > > >
> > > > > Best regards,
> > > > > Pearu
> > > >
>

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