Hpc on Publish Assistant

HPDC 2025 Digest

Sun, 20 Jul 2025 00:00:00 +0000

10 papers selected.

Parameterized Algorithms for Non-uniform All-to-all

Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar

TL;DR — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.

Why notable — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.

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DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge

Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001

IPDPS 2025 Digest

Mon, 19 May 2025 00:00:00 +0000

12 papers selected.

Enhancing OmpSs-2 Suspendable Tasks by Combining Operating System and User-Level Threads with C++ Coroutines

Arnau Cinca, Aleix Roca, Kevin Sala, Raúl Peñacoba Veigas et al.

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.

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.

CCGrid 2024 Digest

Mon, 06 May 2024 00:00:00 +0000

10 papers selected.

Fair, Efficient Multi-Resource Scheduling for Stateless Serverless Functions with Anubis

Amit Samanta 0001, Ryan Stutsman

TL;DR — Anubis introduces a fair, multi-resource scheduler for stateless serverless functions that achieves efficiency without sacrificing isolation between tenants.

Why notable — Fairness in serverless resource allocation is an open problem as functions compete for heterogeneous resources (CPU, memory, I/O); Anubis provides a concrete, deployable answer. The work directly addresses a gap in production FaaS platforms where existing schedulers optimize for throughput but ignore per-tenant equity.