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