project_6/upstream_ref/xllm/docs/en/features/basics.md

# Basics

- xLLM uses a one-device-per-process architecture. Across multiple devices, RPC is used for function calls, and data communication during model computation uses device collective communication libraries.

- HCCL/LCCL are high-performance collective communication frameworks that provide data-parallel and model-parallel collective communication for both single-node multi-device and multi-node multi-device scenarios.
ref(upstream): FULL TREE — Deep-Spark xllm (1470) + ds_vllm csrc/models (703) Replaces cherry-picked upstream_ref with complete source trees. xllm/ — Iluvatar official C++ inference engine (15MB, 1470 files) Complete: kernels → layers → models → runtime → scheduler → api Excluded: .git, binary images, third_party submodule checkouts ds_vllm/ — Iluvatar official vllm fork (8MB, 703 files) Included: csrc/ (ALL CUDA kernels), fused_moe/, qwen3_5 model, _custom_ops Excluded: tests, benchmarks, docs, examples (not needed for reference) Critical call chains now fully traceable: MoE: moe_topk_softmax_kernels.cuh → ixformer.h → fused_moe.cpp → layer GDN: qwen3_gated_delta_net_base.cpp → qwen3_5_gated_delta_net.cpp Attention: ixformer.h → xllm_paged_attention → attention.cpp 2026-08-10 02:53:54 +00:00			`# Basics`

			`- xLLM uses a one-device-per-process architecture. Across multiple devices, RPC is used for function calls, and data communication during model computation uses device collective communication libraries.`

			`- HCCL/LCCL are high-performance collective communication frameworks that provide data-parallel and model-parallel collective communication for both single-node multi-device and multi-node multi-device scenarios.`