161 lines
8.5 KiB
Markdown
161 lines
8.5 KiB
Markdown
---
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title: IPDPS 2025 Digest
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venue: IPDPS
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year: 2025
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date: '2025-05-19'
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tags:
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- parallel-computing
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- hpc
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- distributed-systems
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- gpu
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paper_count: 12
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draft: false
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---
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12 papers selected.
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---
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### Enhancing OmpSs-2 Suspendable Tasks by Combining Operating System and User-Level Threads with C++ Coroutines
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*Arnau Cinca, Aleix Roca, Kevin Sala, Raúl Peñacoba Veigas *et al.**
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**TL;DR** — Extends the OmpSs-2 task-based runtime with C++ coroutines to implement suspendable tasks that can yield while blocked on I/O or communication without stalling the OS thread.
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**Why notable** — Suspendable tasks are a key missing primitive for overlapping computation and communication in task-graph runtimes; the hybrid OS/user-level thread design avoids the overhead of full context switches while remaining portable, with broad implications for OpenMP-style programming on modern heterogeneous nodes.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00015)
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### PISA: An Adversarial Approach to Comparing Task Graph Scheduling Algorithms
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*Jared Coleman, Bhaskar Krishnamachari*
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**TL;DR** — Introduces an adversarial instance-generation framework that automatically synthesizes task graphs that expose worst-case performance gaps between competing scheduling heuristics.
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**Why notable** — Benchmark-driven comparison of DAG schedulers is notoriously biased toward whoever designed the benchmark; PISA's adversarial synthesis provides a principled, algorithm-agnostic methodology that could become a standard evaluation tool for the task-scheduling community.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00014)
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### Parallel Scheduling of Task Graphs with Minimal Memory Requirements
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*Pascal Fradet, Alain Girault, Alexandre Honorat*
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**TL;DR** — Proves tight bounds and provides scheduling algorithms for task graphs that minimize peak memory usage while preserving parallelism, targeting memory-constrained accelerators.
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**Why notable** — Peak memory is increasingly the binding constraint on accelerators with fixed HBM capacity; deriving schedules that are jointly memory-optimal and parallel addresses a theoretically hard trade-off that directly impacts large-scale neural network and sparse-solver pipelines.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00042)
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### FATHOM: Fast Attention Through Optimizing Memory
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*Elliott Binder, Arvind Sudarsanam, Ravi Sunkavalli, Tze Meng Low*
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**TL;DR** — Redesigns the attention kernel memory access pattern to maximize reuse across the Q, K, and V tiles, achieving significant throughput gains over FlashAttention on modern GPUs.
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**Why notable** — Attention is the dominant compute bottleneck in transformer inference and training; FATHOM's memory-centric reformulation improves on the widely deployed FlashAttention baseline and demonstrates that analytical cache-tile reasoning still yields practical gains at scale.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00106)
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### Fast and Effective Lossy Compression on GPUs and CPUs with Guaranteed Error Bounds
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*Alex Fallin, Noushin Azami, Sheng Di, Franck Cappello *et al.**
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**TL;DR** — Presents a portable error-bounded lossy compressor that runs natively on both GPU and CPU, delivering competitive compression ratios with strict point-wise error guarantees and low latency.
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**Why notable** — Scientific simulations increasingly need in-situ compression on the same GPU that runs the simulation; providing tight error bounds alongside GPU portability bridges a critical gap between scientific fidelity requirements and storage bandwidth constraints at exascale.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00083)
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### Phase-Based Frequency Scaling for Energy-Efficient Heterogeneous Computing
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*Lorenzo Carpentieri, Antonio De Caro, Majid Salimi Beni, Kaijie Fan *et al.**
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**TL;DR** — Dynamically scales CPU and GPU frequencies based on detected application phases to reduce energy consumption while preserving performance on heterogeneous nodes.
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**Why notable** — Energy efficiency is a first-class constraint at exascale; phase-aware DVFS that jointly controls both CPU and GPU frequencies avoids the over-provisioning inherent in static policies, with measured gains directly applicable to production HPC clusters.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00078)
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---
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### HiCCL: A Hierarchical Collective Communication Library
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*Mert Hidayetoglu, Simon Garcia de Gonzalo, Elliott Slaughter, Pinku Surana *et al.**
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**TL;DR** — Implements a multi-level collective communication library that decomposes all-reduce and other collectives into hierarchical sub-collectives matched to node-local, intra-rack, and inter-rack bandwidth tiers.
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**Why notable** — Flat NCCL/MPI collectives ignore the hierarchical bandwidth structure of modern GPU clusters; HiCCL's topology-aware decomposition achieves substantial throughput improvements on large distributed training jobs and is designed to be backend-agnostic.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00089)
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### Unified Designs of Multi-Rail-Aware MPI Allreduce and Alltoall Operations Across Diverse GPU and Interconnect Systems
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*Chen-Chun Chen, Jinghan Yao, Lang Xu, Hari Subramoni *et al.**
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**TL;DR** — Develops unified multi-rail-aware algorithms for MPI Allreduce and Alltoall that exploit all available NIC ports simultaneously across heterogeneous interconnect systems.
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**Why notable** — Multi-rail configurations are increasingly common in HPC clusters yet most MPI libraries treat them as a single logical link; this work systematically closes the performance gap and the unified design enables deployment without per-system hand-tuning.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00088)
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### CoRD: Converged RDMA Dataplane
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*Maksym Planeta, Jan Bierbaum, Michael Roitzsch, Hermann Härtig*
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**TL;DR** — Proposes a converged RDMA dataplane that unifies one-sided and two-sided communication semantics over a single kernel-bypass path, reducing software overhead and improving scalability.
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**Why notable** — RDMA stacks remain split between one-sided verbs and two-sided message passing, forcing application writers to choose and preventing optimal use of NIC offload capabilities; CoRD's converged abstraction enables a new class of communication patterns at near-wire speed.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00099)
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### FlexRLHF: A Flexible Placement and Parallelism Framework for Efficient RLHF Training
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*Youshao Xiao, Zhenglei Zhou, Fagui Mao, Weichang Wu *et al.**
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**TL;DR** — Introduces a placement and parallelism co-optimization framework for Reinforcement Learning from Human Feedback (RLHF) training that jointly schedules the actor, critic, and reward models to maximize GPU utilization.
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**Why notable** — RLHF is the dominant fine-tuning paradigm for large language models but its multi-model, heterogeneous-workload structure makes naive data/model parallelism highly inefficient; FlexRLHF's co-placement approach delivers meaningful throughput gains and establishes a design template for future RLHF infrastructure.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00039)
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### GuardianOMP: A Framework for Highly Productive Fault Tolerance Via OpenMP Task-Level Replication
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*Adrian Munera, Eduardo Quiñones, Sara Royuela*
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**TL;DR** — Adds transparent task-level redundant execution to OpenMP applications, enabling automatic detection and recovery from silent data corruption without application source changes.
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**Why notable** — Silent data corruption is an escalating concern as DRAM and compute elements scale into billions of transistors; GuardianOMP's integration at the OpenMP runtime level makes resilience accessible to the broad HPC community that already uses OpenMP without requiring manual checkpoint/restart logic.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00114)
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### Tera-Scale Multilevel Graph Partitioning
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*Daniel Salwasser, Daniel Seemaier, Lars Gottesbüren, Peter Sanders 0001*
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**TL;DR** — Scales multilevel graph partitioning to trillion-edge graphs through a distributed coarsening and refinement pipeline that maintains partition quality competitive with state-of-the-art tools on billion-edge benchmarks.
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**Why notable** — Graph partitioning is a prerequisite for almost every distributed graph workload; reaching the tera-scale regime with near-optimal quality is a significant algorithmic and engineering milestone that directly enables graph-parallel simulation at the largest current HPC scales.
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[→ Read paper](https://doi.org/10.1109/IPDPS64566.2025.00033)
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