dlslime 0.0.1.post2

DLSlime Transfer Engine

DLSlime Transfer Engine

A Peer to Peer RDMA Transfer Engine.

Usage

RDMA READ

• Details in p2p.py

devices = available_nic()
assert devices, "No RDMA devices."

# Initialize RDMA endpoint
initiator = RDMAEndpoint(device_name=devices[0], ib_port=1, link_type="Ethernet")
# Register local GPU memory with RDMA subsystem
local_tensor = torch.tensor(...)
initiator.register_memory_region("buffer", local_tensor...)

# Initialize target endpoint on different NIC
target = RDMAEndpoint(device_name=devices[-1], ib_port=1, link_type="Ethernet")
# Register target's GPU memory
remote_tensor = torch.tensor(...)
target.register_memory_region("buffer", remote_tensor...)

# Establish bidirectional RDMA connection:
# 1. Target connects to initiator's endpoint information
# 2. Initiator connects to target's endpoint information
# Note: Real-world scenarios typically use out-of-band exchange (e.g., via TCP)
target.connect_to(initiator.local_endpoint_info)
initiator.connect_to(target.local_endpoint_info)

# Execute asynchronous batch read operation:
asyncio.run(initiator.async_read_batch("buffer", [0], [8], 8))

SendRecv

• Details in sendrecv.py

Sender

# RDMA init and RDMA Connect just like RDMA Read
...

# RDMA Send
ones = torch.ones([16], dtype=torch.uint8)
endpoint.register_memory_region("buffer", ones.data_ptr(), 16)
asyncio.run(endpoint.send_async("buffer", 0, 8))

Receiver

# RDMA init and RDMA Connect just like RDMA Read
...

# RDMA Recv
zeros = torch.zeros([16], dtype=torch.uint8)
endpoint.register_memory_region("buffer", zeros.data_ptr(), 16)
asyncio.run(endpoint.recv_async("buffer", 8, 8))

Build

# on CentOS
sudo yum install cppzmq-devel gflags-devel  cmake 

# on Ubuntu
sudo apt install libzmq-dev libgflags-dev cmake

# build from source
mkdir build; cd build
cmake -DBUILD_BENCH=ON -DBUILD_PYTHON=ON ..; make

Benchmark

# Target
./bench/transfer_bench                \
  --remote-endpoint=10.130.8.138:8000 \
  --local-endpoint=10.130.8.139:8000  \
  --device-name="mlx5_bond_0"         \
  --mode target                       \
  --block-size=2048000                \
  --batch-size=160

# Initiator
./bench/transfer_bench                \
  --remote-endpoint=10.130.8.139:8000 \ 
  --local-endpoint=10.130.8.138:8000  \ 
  --device-name="mlx5_bond_0"         \
  --mode initiator                    \
  --block-size=16384                  \
  --batch-size=16                     \
  --duration 10

Cross node performance

• H800 with NIC ("mlx5_bond_0"), RoCE v2.

Batch Size	Block Size (Bytes)	Total Trips	Total Transferred (MiB)	Duration (s)	Average Latency (ms/trip)	Throughput (MiB/s)
160	8,192	59,391	74,238	10.0001	0.168377	7,423.8
160	16,384	51,144	127,860	10.0002	0.195530	12,785.8
160	32,768	36,614	183,070	10.0002	0.273124	18,306.7
160	65,536	21,021	210,210	10.0003	0.475729	21,020.4
160	128,000	11,419	223,027	10.0006	0.875789	22,301.3
160	256,000	5,839	228,085	10.0015	1.712880	22,805.2
160	512,000	2,956	230,937	10.0010	3.383300	23,091.3
160	1,024,000	1,486	232,187	10.0006	6.729860	23,217.4
160	2,048,000	742	231,875	10.0010	13.478400	23,185.2