2025. 12. 10. Wednesday

2025. 12. 03. Wednesday * UPMEM PIM Programming- dpu.c : tasklet_id = me(), 한 DPU 내부의 tasklet 구분 - host.c : 서로 다른 DPU 인식 -> 구현을 여기서 수정해야 한다. - 이때 DPU_FOREACH Iterator를 활용한다. - As a consequence, the host API works on sets of DPUs, which may contain multiple DPU ranks.- The provided C macro DPU_RANK_FOREACH and DPU_FOREACH iterate over the ranks and DPUs respectively of a set. # UPMEM-GEM..

2025. 11. 29 Saturday 한 달 내내 풀지 못했던 에러를 해결했다.!너무너무너무 기뻤다. .·⋆(⌒_⌒)⋆·. 오랜만에 느껴본 코딩(디버깅)의 맛 .막막하고 어려운 문제도 step by step으로생각해보면, 찬찬히 해결할 수 있다 ! * dpu-diag- 총 2546개 DPU, 350Mhz를 확인할 수 있다. - perfcounter_get 로 가져온 cycle을 ms로 표현할 때, 정확한 hz가 필요하다. * Cycles 선형 증가 해결 - perfcounter_config(COUNT_CYCLES, true); - 초기화 구문 추가로, head 별 cycle 에러 해결 * DPU 내부 float 연산 제거 - 구조체 정의 변경, PIM 성능 향상 + TILE_ROWS 최적값..

2025. 10. 29. Wednesday 📌 TODOLIST - quantization 적용 더 알아보기 (float -> int) - dpu 2nd tasklet error debugging - embedding 차원 조절하기 (tasklet >= 16)

2025. 10. 11. SaturdayUPMEM Official Example Review

2025. 10. 10. Friday다음주 랩미팅 준비: Code Review · 구현 📌 참고자료 - UPMEM SDK: https://sdk.upmem.com/stable/030_DPURuntimeService_Tasklets.html- UPMEM Naive-GEMM: https://github.com/hhessammheidary/UPMEM-GEMM 📌 TODOLIST- UPMEM Checksum example Code Review- PIM Embedding Lookup(Python Wrapper) - MHA Implementation

2025. 09. 30. 화요일

UPMEM Hello World! Examplehttps://sdk.upmem.com/stable/02_HelloWorld.html Hello World! Example — UPMEM DPU SDK 2025.1.0 Documentation© Copyright 2015-2024, UPMEM SAS - All rights reserved.sdk.upmem.com 0. UPMEM SDK 설치하기 https://sdk.upmem.com UPMEM DPU SDKUPMEM SDK The Software Development Kit for programming and using the DPU provided by the UPMEM Acceleration platform.sdk.upmem.com tar -..

PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing Systemhttps://arxiv.org/abs/2502.15470 PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing SystemLarge language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies ..

Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Servinghttps://github.com/casys-kaist/pimba GitHub - casys-kaist/pimba: Official code repository for "Pimba: A Processing-in-Memory Acceleration for Post-Transformer LargeOfficial code repository for "Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving [MICRO'25]" - casys..