🖥️ Multi-GPU Simulation

Genesis supports multi-GPU execution for scaling simulations.

Single GPU Configuration

import genesis as gs

# Automatic GPU selection
gs.init(backend=gs.gpu)

# Force specific backend
gs.init(backend=gs.cuda)   # NVIDIA CUDA
gs.init(backend=gs.metal)  # Apple Metal
gs.init(backend=gs.cpu)    # CPU fallback

Parallel Environments (Single GPU)

Scale by batching environments on one GPU:

scene.build(n_envs=2048, env_spacing=(1.0, 1.0))
# All environments run in parallel on same GPU

Multi-GPU with Multiprocessing

Run separate processes per GPU:

import os
import multiprocessing

def run_simulation(gpu_id):
    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
    os.environ["TI_VISIBLE_DEVICE"] = str(gpu_id)
    os.environ["EGL_DEVICE_ID"] = str(gpu_id)

    import genesis as gs
    gs.init(backend=gs.gpu)
    # ... simulation code ...

if __name__ == "__main__":
    for i in range(2):  # 2 GPUs
        p = multiprocessing.Process(target=run_simulation, args=(i,))
        p.start()

Distributed Training (DDP)

Use PyTorch Distributed Data Parallel:

torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py

import os
import torch
import torch.distributed as dist
import genesis as gs

local_rank = int(os.environ.get("LOCAL_RANK", 0))
os.environ["CUDA_VISIBLE_DEVICES"] = str(local_rank)
os.environ["TI_VISIBLE_DEVICE"] = str(local_rank)

gs.init(backend=gs.gpu, seed=local_rank)
scene.build(n_envs=2048)

torch.cuda.set_device(0)
dist.init_process_group(backend="nccl", init_method="env://")
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[0])

# Training loop with gradient synchronization
for step in range(steps):
    scene.step()
    loss.backward()  # DDP handles all-reduce
    optimizer.step()

dist.barrier()
dist.destroy_process_group()

Environment Variables

Variable	Purpose
CUDA_VISIBLE_DEVICES	PyTorch/CUDA GPU selection
TI_VISIBLE_DEVICE	Taichi GPU selection
EGL_DEVICE_ID	Rendering GPU (OpenGL/EGL)

Always set all three together for multi-GPU setups.

GPU Selection Patterns

Pattern	Method	GPUs	Complexity
Single GPU	gs.init(backend=gs.gpu)	1	Low
Batched envs	scene.build(n_envs=N)	1	Low
Multi-process	Multiprocessing + env vars	N	Medium
Distributed	torchrun + DDP	N	High

Best Practices

1. Batch first: Use large n_envs on single GPU before scaling to multi-GPU

2. Set all env vars: Always set CUDA, Taichi, and EGL device together

3. Synchronize DDP: Call dist.barrier() before destroying process groups

4. Headless rendering: Set pyglet.options["headless"] = True on servers

5. Monitor memory: Use nvidia-smi during batched simulation

Device Access

After initialization:

gs.device    # PyTorch device (e.g., "cuda:0", "mps:0")
gs.backend   # Backend type (gs.cuda, gs.metal, gs.cpu)