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venue: HPDC
year: 2024
papers:
- title: Efficient all-to-all Collective Communication Schedules for Direct-connect
Topologies
authors:
- Prithwish Basu
- Liangyu Zhao
- Jason Fantl
- Siddharth Pal
- Arvind Krishnamurthy
- Joud Khoury
reason: "Derives near-optimal all-to-all collective communication schedules for direct-connect HPC topologies, directly improving bandwidth utilization in large-scale distributed systems."
- title: Reinforcement Learning-based Adaptive Mitigation of Uncorrected DRAM Errors
in the Field
authors:
- Isaac Boixaderas
- Sergi Moré
- Javier Bartolome
- David Vicente
- Petar Radojkovic
- Paul M. Carpenter
- Eduard Ayguadé
reason: "Applies reinforcement learning to dynamically mitigate uncorrected DRAM errors at production HPC scale, improving system reliability without sacrificing performance."
- title: 'IDT: Intelligent Data Placement for Multi-tiered Main Memory with Reinforcement
Learning'
authors:
- Juneseo Chang
- Wanju Doh
- Yaebin Moon
- Eojin Lee
- Jung Ho Ahn
reason: "Presents a reinforcement learning-driven runtime that automatically places data across heterogeneous memory tiers, reducing access latency in HPC nodes with complex memory hierarchies."
- title: 'FaaSKeeper: Learning from Building Serverless Services with ZooKeeper as
an Example'
authors:
- Marcin Copik
- Alexandru Calotoiu
- Pengyu Zhou
- Konstantin Taranov
- Torsten Hoefler
reason: "Reconstructs ZooKeeper as a fully serverless service and distills concrete design lessons for building stateful distributed coordination primitives on FaaS platforms."
- title: 'ESG: Pipeline-Conscious Efficient Scheduling of DNN Workflows on Serverless
Platforms with Shareable GPUs'
authors:
- Xinning Hui
- Yuanchao Xu 0001
- Zhishan Guo
- Xipeng Shen
reason: "Introduces a pipeline-aware scheduler that shares GPUs across serverless DNN workflow stages, substantially cutting end-to-end latency and GPU idle time."
- title: 'FASOP: Fast yet Accurate Automated Search for Optimal Parallelization of
Transformers on Heterogeneous GPU Clusters'
authors:
- Sunyeol Hwang
- Eungyeong Lee
- Hongseok Oh 0003
- Youngmin Yi
reason: "Provides a fast, model-driven search strategy that finds optimal tensor/pipeline/data parallelism configurations for transformer training on heterogeneous GPU clusters."
- title: Near-Optimal Wafer-Scale Reduce
authors:
- Piotr Luczynski
- Lukas Gianinazzi
- Patrick Iff
- Leighton Wilson
- Daniele De Sensi
- Torsten Hoefler
reason: "Designs and analyzes near-optimal Reduce collective algorithms tailored to wafer-scale interconnect topology, setting new performance bounds for next-generation HPC hardware."
- title: 'DataStates-LLM: Lazy Asynchronous Checkpointing for Large Language Models'
authors:
- Avinash Maurya
- Robert Underwood
- M. Mustafa Rafique
- Franck Cappello
- Bogdan Nicolae
reason: "Introduces lazy asynchronous checkpointing that overlaps LLM training with I/O, dramatically reducing checkpoint overhead on large-scale HPC storage systems."
- title: 'ADTopk: All-Dimension Top-k Compression for High-Performance Data-Parallel
DNN Training'
authors:
- Zhangqiang Ming
- Yuchong Hu
- Wenxiang Zhou
- Xinjue Zheng
- Chenxuan Yao
- Dan Feng 0001
reason: "Proposes an all-dimension top-k gradient sparsification scheme that reduces communication volume in data-parallel distributed training while preserving convergence quality."
- title: Accelerating Function-Centric Applications by Discovering, Distributing,
and Retaining Reusable Context in Workflow Systems
authors:
- Thanh Son Phung
- Colin Thomas
- Logan T. Ward
- Kyle Chard
- Douglas Thain
reason: "Introduces context reuse across scientific workflow tasks, allowing distributed workflow systems to cache and share intermediate computation artifacts and significantly reduce redundant work."
- title: 'CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras
CS-2'
authors:
- Shihui Song
- Yafan Huang
- Peng Jiang 0004
- Xiaodong Yu 0001
- Weijian Zheng
- Sheng Di
- Qinglei Cao
- Yunhe Feng
- Zhen Xie
- Franck Cappello
reason: "Ports and scales error-bounded lossy compression to the Cerebras CS-2 wafer-scale engine, enabling significant data reduction for HPC scientific workloads on novel accelerator hardware."
- title: 'EvoStore: Towards Scalable Storage of Evolving Learning Models'
authors:
- Robert Underwood
- Meghana Madhyastha
- Randal C. Burns
- Bogdan Nicolae
reason: "Designs a storage system that efficiently manages the versioned, incrementally evolving checkpoints produced during large-scale distributed model training, reducing storage overhead and retrieval time."