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 <wesmck...@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.peter...@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 <wesmck...@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.peter...@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 > > >
