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title: IPDPS 2024 Digest
venue: IPDPS
year: 2024
date: '2024-01-01'
tags: []
paper_count: 14
draft: false
---
14 papers selected.
---
### Low-Depth Spatial Tree Algorithms
*Yves Baumann, Tal Ben-Nun, Maciej Besta, Lukas Gianinazzi *et al.**
**TL;DR** — Introduces parallel spatial-tree algorithms with provably low depth, advancing the theory of work-efficient parallel data structures for geometric workloads.
---
### Alternative Basis Matrix Multiplication is Fast and Stable
*Oded Schwartz, Sivan Toledo, Noa Vaknin, Gal Wiernik*
**TL;DR** — Demonstrates that alternative-basis matrix multiplication achieves both practical speed and numerical stability, challenging the conventional trade-off between the two.
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### Wait-free Trees with Asymptotically-Efficient Range Queries
*Ilya Kokorin, Victor Yudov, Vitaly Aksenov, Dan Alistarh*
**TL;DR** — Presents the first wait-free balanced search tree supporting asymptotically optimal range queries, a long-standing open problem in concurrent data structures.
---
### Parallel Derandomization for Coloring
*Sam Coy, Artur Czumaj, Peter Davies-Peck, Gopinath Mishra*
**TL;DR** — Develops deterministic parallel graph-coloring algorithms via derandomization, closing a key gap between randomized and deterministic complexity in this foundational problem.
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### HINT: Designing Cache-Efficient MPI_Alltoall using Hybrid Memory Copy Ordering and Non-Temporal Instructions
*Bharath Ramesh 0005, Nick Contini, Nawras Alnaasan, Kaushik Kandadi Suresh *et al.**
**TL;DR** — Achieves substantial MPI_Alltoall bandwidth improvements by combining cache-aware copy ordering with non-temporal store instructions, directly benefiting large-scale collective communication.
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### An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression
*Jiajun Huang 0001, Sheng Di, Xiaodong Yu 0001, Yujia Zhai *et al.**
**TL;DR** — Integrates error-bounded lossy compression directly into MPI collectives, reducing communication volume with provable accuracy guarantees for HPC scientific applications.
---
### Software Resource Disaggregation for HPC with Serverless Computing
*Marcin Copik, Marcin Chrapek, Larissa Schmid, Alexandru Calotoiu *et al.**
**TL;DR** — Shows that serverless computing can serve as a practical resource-disaggregation layer for HPC, enabling fine-grained elasticity without sacrificing performance.
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### Tackling Cold Start in Serverless Computing with Multi-Level Container Reuse
*Amelie Chi Zhou, Rongzheng Huang, Zhoubin Ke, Yusen Li *et al.**
**TL;DR** — Proposes a multi-level container-reuse strategy that significantly reduces cold-start latency in serverless platforms, addressing one of the main performance bottlenecks.
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### LightDAG: A Low-latency DAG-based BFT Consensus through Lightweight Broadcast
*Xiaohai Dai, Guanxiong Wang, Jiang Xiao 0001, Zhengxuan Guo *et al.**
**TL;DR** — Redesigns DAG-based Byzantine fault-tolerant consensus to use lightweight broadcast, cutting latency while preserving safety and liveness in distributed systems.
---
### Benchmarking and Dissecting the Nvidia Hopper GPU Architecture
*Weile Luo, Ruibo Fan, Zeyu Li, Dayou Du *et al.**
**TL;DR** — Provides the first systematic microbenchmark characterization of Hopper's new hardware features (TMA, warpgroup MMA, NVLink-4), yielding actionable insights for kernel developers.
---
### DEFCON: Deformable Convolutions Leveraging Interval Search and GPU Texture Hardware
*Malith Jayaweera, Yanyu Li, Yanzhi Wang 0001, Bin Ren 0002 *et al.**
**TL;DR** — Exploits GPU texture-cache hardware to accelerate deformable convolutions, delivering significant speedups over cuDNN-based baselines for irregular memory-access patterns.
---
### nOS-V: Co-Executing HPC Applications Using System-Wide Task Scheduling
*David Álvarez 0006, Kevin Sala, Vicenç Beltran 0001*
**TL;DR** — Introduces a system-wide task scheduler that safely co-executes multiple HPC applications on shared hardware, improving cluster utilization without modifying application code.
---
### Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster
*Abeda Sultana, Fei Xu, Xu Yuan 0001, Li Chen 0019 *et al.**
**TL;DR** — Formulates deep-learning cluster scheduling as an online optimization problem that explicitly accounts for GPU heterogeneity, reducing job completion times and improving fairness.
---
### A Parallel Partial Merge Repair Algorithm for Multi-block Failures for Erasure Storage Systems
*Shuaipeng Zhang, Shiyi Li, Chentao Wu, Ruobin Wu *et al.**
**TL;DR** — Presents a parallel repair algorithm for simultaneous multi-block erasure failures that outperforms sequential recovery while reducing I/O and computational overhead.