hi Pearu -- yes, I had thought of this work working on the arrow_gpu library. Some time ago I opened
https://issues.apache.org/jira/browse/ARROW-1470 thinking that it would be good to combine the MemoryPool* concept and the AllocateBuffer concept into a single abstract interface. Such an interface for CUDA could also optimize small allocations by allocating larger "pages" if desired. So Before adding a CudaMemoryPool we should consider if we want to define a BufferAllocator interface On Thu, Oct 4, 2018 at 5:04 AM Pearu Peterson <pearu.peter...@quansight.com> wrote: > > Hi, > Currently, the arrow host memory management includes MemoryPool to > accelerate memory operations (new/free). > Would there be interest in supporting the same concept in CUDA memory > management to reduce the overhead of cudaMalloc/cudaFree? > Best regards, > Pearu > > On Wed, Oct 3, 2018 at 11:44 PM Pearu Peterson <pearu.peter...@quansight.com> > wrote: > > > 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 <wesmck...@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 <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 > >> > > > > >> > >
