2nd/10_Wiki/Topic_Programming/Architecture/Parallel-Computing-in-AI.md

---
id: wiki-2026-0508-parallel-computing-in-ai
title: Parallel Computing in AI
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI parallelism, distributed AI training, AI parallel computing]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [parallelism, ai, distributed, gpu, training]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: pytorch
---

# Parallel Computing in AI

## 매 한 줄
> **"매 AI 의 parallel 은 단순 distributed 가 아니라 4축 (Data, Tensor, Pipeline, Expert) × 2 모드 (training/inference) 의 조합 problem"**. 매 2026 Llama 3 405B 학습 = 16384 GPU, GPT-5 추론 = 매 expert sharding + speculative decoding. 매 parallel strategy 의 mismatch = 매 ROI 폭락.

## 매 핵심

### 매 4 축
1. **Data Parallel (DP)**: 매 batch 분할, 매 weight 동기.
2. **Tensor Parallel (TP)**: 매 single layer 의 weight matrix 분할.
3. **Pipeline Parallel (PP)**: 매 layer stack 분할.
4. **Expert Parallel (EP)**: 매 MoE 의 expert 분할.

### 매 + 보조 axis
- **Sequence Parallel (SP)**: 매 long context 의 token 차원 분할 (Ring Attention, Ulysses).
- **Context Parallel (CP)**: 매 attention 의 KV partial.
- **ZeRO sharding (DP variant)**: 매 optimizer/grad/param 분할.

### 매 training vs inference
| 측면 | Training | Inference |
|---|---|---|
| 주축 | DP + ZeRO | TP + EP |
| Batch | 큰 micro-batch | 매 1 user → 매 small |
| Memory | activation 중심 | KV cache 중심 |
| Comm | all-reduce (grad) | all-reduce (TP), all-to-all (MoE) |
| 도구 | DeepSpeed, Megatron, FSDP | vLLM, TensorRT-LLM, SGLang |

### 매 hardware constraint
- **NVLink** (intra-node, 900GB/s): 매 TP 친화.
- **InfiniBand / NVLink Switch** (inter-node, 400Gbps NDR): 매 PP/DP.
- **HBM3e** (192GB on B200): 매 더 큰 model fit + 매 sharding 절약.

## 💻 패턴

### FSDP2 (PyTorch 2.6+, 2026)
```python
import torch
from torch.distributed.fsdp import fully_shard, MixedPrecisionPolicy

model = build_transformer()
mp = MixedPrecisionPolicy(param_dtype=torch.bfloat16, reduce_dtype=torch.float32)

for layer in model.layers:
    fully_shard(layer, mp_policy=mp)
fully_shard(model, mp_policy=mp)

opt = torch.optim.AdamW(model.parameters(), lr=3e-4, fused=True)
for batch in loader:
    loss = model(batch).loss
    loss.backward()
    opt.step(); opt.zero_grad()
```

### TP with DTensor / device_mesh
```python
from torch.distributed.tensor import DTensor, Shard, Replicate
from torch.distributed.device_mesh import init_device_mesh

mesh = init_device_mesh("cuda", (DP, TP), mesh_dim_names=("dp","tp"))

# Column-parallel linear (매 weight col-sharded)
w = nn.Parameter(torch.empty(out, in_))
w_dt = DTensor.from_local(w, mesh["tp"], [Shard(0)])
# 매 forward: x @ w_dt.T → 매 partial output, all-reduce 자동
```

### MoE expert parallel (DeepSpeed-MoE / Megatron)
```python
class MoELayer(nn.Module):
    def __init__(self, experts, top_k=2):
        super().__init__()
        self.experts = nn.ModuleList(experts)  # 매 sharded across EP group
        self.gate = nn.Linear(d, len(experts))
        self.k = top_k
    def forward(self, x):
        logits = self.gate(x)
        topk = logits.topk(self.k, dim=-1)
        # 매 all-to-all dispatch tokens to expert-owning rank
        dispatched = all_to_all_dispatch(x, topk.indices, ep_group)
        out = local_experts(dispatched)
        return all_to_all_combine(out, topk.indices, topk.values, ep_group)
```

### vLLM tensor-parallel inference
```python
from vllm import LLM, SamplingParams

llm = LLM(
    model="meta-llama/Llama-3.3-70B-Instruct",
    tensor_parallel_size=4,        # 매 4 GPU TP
    pipeline_parallel_size=1,
    gpu_memory_utilization=0.92,
    enable_chunked_prefill=True,
    enable_prefix_caching=True,
)
out = llm.generate(["매 안녕"], SamplingParams(temperature=0.7, max_tokens=128))
```

### Ring Attention (sequence parallel for 1M context)
```python
def ring_attention(q, k, v, sp_group):
    # 매 each rank holds 1/N of sequence
    out = torch.zeros_like(q); lse = torch.full(...)
    k_blk, v_blk = k, v
    for step in range(world_size(sp_group)):
        partial, l = flash_attn_partial(q, k_blk, v_blk)
        out, lse = log_sum_exp_combine(out, lse, partial, l)
        k_blk, v_blk = ring_send_recv(k_blk, v_blk, sp_group)
    return out
```

### 3D parallel mesh (Megatron-Core 2026)
```python
mesh = init_device_mesh(
    "cuda", (DP_replica, DP_shard, PP, TP),
    mesh_dim_names=("dp_r","dp_s","pp","tp"),
)
# Llama 3 405B: dp_r=16, dp_s=8, pp=16, tp=8 → 16384 GPUs
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| ≤ 8B model, 1 node | DP + ZeRO-2 |
| 70B, multi-node | FSDP2 (DP shard) + TP intra-node |
| 405B+ | TP × PP × DP (3D) |
| MoE (Mixtral, GPT-5 style) | + EP (all-to-all) |
| 1M+ context | + SP (Ring/Ulysses) |
| Inference 1 user | TP only, no DP |
| Inference batch | TP + continuous batching (vLLM) |
| MoE inference | EP + speculative decoding |

**기본값**: 매 training = FSDP2 + TP, 매 inference = vLLM TP.

## 🔗 Graph
- 부모: [[Distributed Computing]]
- 변형: [[Pipeline Parallelism]]
- 응용: [[LLM_Optimization_and_Deployment_Strategies|vLLM]]
- Adjacent: [[Ring Attention]] · [[Mixture of Experts]]

## 🤖 LLM 활용
**언제**: 매 model 또는 batch 가 single-GPU mem 초과. 매 throughput / latency SLO 의 multi-GPU 필요.
**언제 X**: 매 single GPU fits + 매 batch latency 만족 — 매 multi-GPU overhead 가 손해.

## ❌ 안티패턴
- **TP across nodes**: 매 IB 의 NVLink 대비 5-10x 느림 → 매 stall.
- **PP without DP**: 매 batch 의 micro-batch 한계 → 매 throughput cap.
- **MoE EP without all-to-all opt**: 매 NCCL all-to-all 의 성능 ↓ → 매 GroupedGEMM kernel 필요.
- **Sequence parallel without flash-attn**: 매 attention recompute 폭증.
- **Mixed precision without loss scaling** (FP16): 매 underflow → 매 loss NaN. → BF16 권장.

## 🧪 검증 / 중복
- Verified (Megatron-LM paper, FSDP2 docs 2026, vLLM docs, DeepSpeed-MoE paper).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 4-axis parallelism + training/inference split |