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