GPU-Aware Communication

Goal: Use UCX for both intra-node and inter-node GPU communication.

Building and Running on OLCF Summit

Prerequisites: OpenPMIx, CUDA-enabled UCX

Building OpenPMIx

- Requires libevent
    - wget https://github.com/libevent/libevent/releases/download/release-2.1.12-stable/libevent-2.1.12-stable.tar.gz
    - tar -xf libevent-2.1.12-stable.tar.gz
    - cd libevent-2.1.12-stable
    - mkdir build && mkdir install
    - cd build
    - ../configure --prefix=$HOME/libevent-2.1.12-stable/install
    - make -j && make install
- wget https://github.com/openpmix/openpmix/releases/download/v3.1.5/pmix-3.1.5.tar.gz
- tar -xf pmix-3.1.5.tar.gz
- cd pmix-3.1.5
- mkdir build install
- cd build
- ../configure --prefix=$HOME/work/pmix-3.1.5/install --with-libevent=$HOME/libevent-2.1.12-stable/install
- make -j && make install

Building CUDA-enabled UCX

Commit 971aad12d142341770c8f918cb91727cd180cb31 of master branch recommended, v1.9.0 has issues with ucx_perftest on Summit, latest commit breaks CUDA linkage somehow.

- git clone git@github.com:openucx/ucx.git
- cd ucx
- git checkout 971aad12d142341770c8f918cb91727cd180cb31
- ./autogen.sh
- mkdir build install
- cd build
- ../contrib/configure-release --prefix=$HOME/ucx/install --with-cuda=$CUDA_DIR --with-gdrcopy=/sw/summit/gdrcopy/2.0
- make -j
- make install

Building Charm4Py with UCX

The following diff should be applied to the Charm++ repository, and the paths specified should be changed to the local installation: location

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 724e6d8d7..70703c450 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -865,7 +865,7 @@ if(${TARGET} STREQUAL "charm4py")
   set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
   add_library(charm SHARED empty.cpp)
   set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib_so)
-  target_link_libraries(charm ck converse memory-default threads-default ldb-rand "-Llib/ -standalone -whole-archive -c++stl -shared")
+  target_link_libraries(charm ck converse memory-default threads-default ldb-rand "-L/ccs/home/jchoi/work/ucx-1.9.0/install/lib -L/ccs/home/jchoi/work/pmix-3.1.5/install/lib -L/sw/summit/gdrcopy/2.0/lib64 -Llib/ -lpmix -lucp -lucm -lucs -luct -lgdrapi -standalone -whole-archive -c++stl -shared")

   add_dependencies(charm hwloc)
 endif()
(charm4py) [zanef2@login1]~/charms/charm% git diff > diff
(charm4py) [zanef2@login1]~/charms/charm% cat diff
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 724e6d8d7..70703c450 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -865,7 +865,7 @@ if(${TARGET} STREQUAL "charm4py")
   set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
   add_library(charm SHARED empty.cpp)
   set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib_so)
-  target_link_libraries(charm ck converse memory-default threads-default ldb-rand "-Llib/ -standalone -whole-archive -c++stl -shared")
+  target_link_libraries(charm ck converse memory-default threads-default ldb-rand "-L/ccs/home/jchoi/work/ucx-1.9.0/install/lib -L/ccs/home/jchoi/work/pmix-3.1.5/install/lib -L/sw/summit/gdrcopy/2.0/lib64 -Llib/ -lpmix -lucp -lucm -lucs -luct -lgdrapi -standalone -whole-archive -c++stl -shared")

   add_dependencies(charm hwloc)
 endif()

The install directories of OpenPMIx and UCX should be passed in with --basedir.

./build charm4py ucx-linux-ppc64le cuda openpmix -j -g --with-production --basedir=$HOME/work/pmix-3.1.5/install --basedir=$HOME/work/ucx-master/install

Then, Charm4Py can be installed normally:

python3 -m pip install --user .

Running Charm4Py with UCX

You can check if UCX is picking up CUDA and GDRCOPY modules properly on the compute nodes by running jsrun -n1 ucx_info -d | grep cuda and jsrun -n1 ucx_info -d | grep gdr.

You may need to pass in --smpiargs="-disable_gpu_hooks" to jsrun if you observe any CUDA hook library failure messages.

Running the Charm4Py GPU latency benchmark (between 2 GPUs, intra-socket): jsrun -n2 -a1 -c2 -g1 -K2 -r2 --smpiargs="-disable_gpu_hooks" ./latency +ppn 1 +pemap L0,8 +commap L4,12

You can change the rendezvous threshold by using the UCX_RNDV_THRESH environment variable. The values that I found to work best for the OSU benchmarks are 131072 for intra-socket, 65536 for inter-socket, and 524288 for inter-node. Note that a too small value (less than 64 in my tests) will cause hangs, probably due to the UCX layer implementation in Charm++.

Charm4Py API

The Charm4Py implementation uses the Channels API. When a channel has been created between chares, there are two options for sending GPU-direct messages: by passing the buffers themselves and by passing arrays containing the pointers/sizes of the arrays. The latter is an optimization when the same buffers are used for communication multiple times, as the cost of determining the address/size of the buffers is paid only once; this optimiziation saves ~20us for each message.

Direct Access

Assume that partner_channel is a channel between two chares, and that d_data_send and d_data_recv are arrays implementing the CUDA Array Interface. To send these arrays through the channel, the following can be used:

# Called on the sender:
partner_channel.send(d_data_send)

# Called on the receiver:
partner_channel.recv(d_data_recv)

Note that multiple arrays can be sent, and that combinations of GPU and host parameters are allowed.

Persistent Communication Optimization

The Direct Access method extracts the address and size of each array using the CUDA Array Interface. Many applications use the same buffer for communication many times, and using the Direct Access the address and size must be extracted each time the Array is used. While we plan to implement a cache to optimize for these situations, we currently offer a workaround that allows to provide this information to the runtime system.

d_data_recv_addr = array.array('L', [0])
d_data_recv_size = array.array('i', [0])
d_data_send_addr = array.array('L', [0])
d_data_send_size = array.array('i', [0])

partner_channel.send(src_ptrs = d_data_send_addr, src_sizes = d_data_send_size)
partner_channel.recv(post_addresses = d_data_recv_addr,
                                   post_sizes = d_data_recv_size
                                  )

References

https://github.com/openucx/ucx/wiki/NVIDIA-GPU-Support https://openucx.readthedocs.io/en/master/faq.html#working-with-gpu

charmplusplus / charm4py