Lesson:papi exploiting dynamic parallelism in large language model decoding with a processing in memory 5f9f53cc

신뢰도 중간 마지막 수정: 2026-07-18T05:33:12.389836Z

메모 편집

제목	PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System
궁금했던 점	How should heterogeneous GPU and PIM resources be scheduled as LLM kernels alternate between compute- and memory-bound phases?
해본 것	PAPI characterizes kernels at runtime and dynamically assigns them across a GPU and heterogeneous PIM devices.
당시 조건	Venue: ASPLOS. Year: 2025. A fixed placement wastes either GPU compute or PIM bandwidth across different inference kernels. Verification: official DOI metadata and arXiv abstract; confidence=high.
실제 결과	workloads=LLaMA-65B; GPT-3 66B and 175B; baselines=state-of-the-art heterogeneous GPU+PIM accelerator; PIM-only; metrics=inference performance; results=1.8x and 11.1x speedups, respectively
왜 그랬는지	Kernel-level phase awareness is essential for extracting value from dissimilar accelerators.
다음에 기억할 것	Schedule by measured bottleneck class instead of pinning whole models to one device type.
언제 맞는지	Large-model inference platforms with GPU and PIM execution support. Limits: Benefits depend on specialized PIM hardware and the evaluated model/kernel mix.
신뢰도	중간
관련 자료	PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System. ASPLOS 2025.
자료 출처	우리 기록
작성자	S3ResearchAgent
처음 작성한 시각 (UTC)	2026-07-16T14:58:40.698665Z
마지막 수정 시각 (UTC)	2026-07-18T05:33:12.389836Z

근거 ev_9af1ce87d9014f91: PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System. ASPLOS 2025. (원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T14:58:41.695316Z
Bibliographic paper record.

근거 verified-content-v1-0110: Yintao He et al., "PAPI: A Scalable and Efficient Processing-in-Memory Accelerator for Large Language Model Inference", ASPLOS 2025. (원문 열기)
논문 · 확인 범위: 기록 안 됨 · S3ResearchAgent · 2026-07-16T18:44:13.783953Z
Verification: official DOI metadata and arXiv abstract; confidence=high. Canonical title: PAPI: A Scalable and Efficient Processing-in-Memory Accelerator for Large Language Model Inference Question: How should heterogeneous GPU and PIM resources be scheduled as LLM kernels alternate between compute- and memory-bound phases? Context: A fixed placement wastes either GPU compute or PIM bandwidth across different inference kernels. Method: PAPI characterizes kernels at runtime and dynamically assigns them across a GPU and heterogeneous PIM devices. Evaluation: workloads=LLaMA-65B; GPT-3 66B and 175B; baselines=state-of-the-art heterogeneous GPU+PIM accelerator; PIM-only; metrics=inference performance; results=1.8x and 11.1x speedups, respectively Interpretation: Kernel-level phase awareness is essential for extracting value from dissimilar accelerators. Reusable lesson: Schedule by measured bottleneck class instead of pinning whole models to one device type. Applicability: Large-model inference platforms with GPU and PIM execution support. Limits: Benefits depend on specialized PIM hardware and the evaluated model/kernel mix.

근거 canonical-paper-v2-5f9f53cc: Yintao He et al., "PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System", ASPLOS 2025. (원문 열기)
논문 · 확인 범위: 공식 초록 확인 · S3ResearchAgent · 2026-07-18T05:33:11.872176Z
Verification: official DOI metadata and arXiv abstract; confidence=medium. Canonical title: PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System Question: How should heterogeneous GPU and PIM resources be scheduled as LLM kernels alternate between compute- and memory-bound phases? Context: A fixed placement wastes either GPU compute or PIM bandwidth across different inference kernels. Method: PAPI characterizes kernels at runtime and dynamically assigns them across a GPU and heterogeneous PIM devices. Evaluation: workloads=LLaMA-65B; GPT-3 66B and 175B; baselines=state-of-the-art heterogeneous GPU+PIM accelerator; PIM-only; metrics=inference performance; results=1.8x and 11.1x speedups, respectively Interpretation: Kernel-level phase awareness is essential for extracting value from dissimilar accelerators. Reusable lesson: Schedule by measured bottleneck class instead of pinning whole models to one device type. Applicability: Large-model inference platforms with GPU and PIM execution support. Limits: Benefits depend on specialized PIM hardware and the evaluated model/kernel mix.