Vincent Lannurien
d822cdaa6a
All checks were successful
Build and deploy static pages / build-and-push (push) Successful in 19s
125 lines
4.7 KiB
Markdown
125 lines
4.7 KiB
Markdown
---
|
|
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.
|
|
|
|
---
|
|
|
|
### 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.
|
|
|
|
---
|
|
|
|
### 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.
|
|
|
|
---
|
|
|
|
### 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.
|
|
|
|
---
|
|
|
|
### 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.
|
|
|
|
---
|
|
|
|
### 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.
|
|
|