cs.DC · 2026-07-03 · No. 42
Distributed, Parallel, and Cluster Computing, 2026-07-03.
9 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
9 entries-
01
FlintKV: A Fast Durable Storage Engine for Modern Databases
Sergey Egorov, Gregory Chockler, Brijesh Dongol, Dan O'Keeffe, Sadegh Keshavarzi
cs.DC
Byte-addressable non-volatile memory (NVM) offers an opportunity to rethink storage engine architectures. While recent NVM key-value stores achieve high throughput for ingestion and point lookups, they omit or under-specify the support for the richer interface guarantees required by modern databases. Production key-value engines (e.g., RocksDB) provide point-in-time snapshots, consistent iterators, and atomic batches-features essential for...
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02
WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
Mauricio Fadel Argerich, Jonathan Fürst, Marta Patiño-Martínez
cs.DC · cs.LG
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination. While some predictive models exist, they still require profiling data and struggle to generalize to hardware unseen during training. To address...
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03
Elasticity in Parallel Sparse Triangular Solve
Raphael S. Steiner, Christos K. Matzoros, Pál András Papp, Toni Böhnlein, A. N. Yzelman
cs.DC
We introduce stale synchronous parallel as a mode of execution in parallel sparse triangular linear system solve and present a general directed-acyclic-graph scheduler capable of producing such schedules. Stale-synchronous-parallel schedules allow the overlap of synchronisation and compute which results in a geometric-mean speed-up of $7$-$30\%$ of our scheduler, ElasticDivide, over state-of-the-art synchronous scheduler GrowLocal on an ARM...
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04
Securing People and their Machines Against Major Faults
Ohad Eitan, Idit Keidar, Ehud Shapiro
cs.DC · cs.CR · cs.MA
We consider grassroots platforms -- distributed systems of agents consisting of people identified by self-chosen public keys and their machines (smartphones) -- and wish to make them secure against \emph{major faults}: the loss of their private keys and/or their smartphones. As grassroots platforms have no global resource to rely on for recovery, our peer-based solution is based on: (\ia) \emph{a grassroots social graph} in which agents...
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05
Cadence: Extreme Pipelining with Multiple Concurrent Proposers
Fatima Elsheimy, Mohammad Mussadiq Jalalzai, Tobias Klenze, Jovan Komatovic, Mike Setrin, Victor Shoup, Kushal...
cs.DC
We present Cadence, a Byzantine fault-tolerant multi-proposer consensus protocol with arbitrarily low block intervals, optimal resilience, and optimal fast-path latency. Cadence divides time into equally spaced slots, one block per slot, each finalized in its own consensus instance. Blocks do not build directly on their predecessor, so instances run independently and none waits for an earlier block to finish or propagate; we call this extreme...
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06
Towards Load-Aware Prefill Deflection for Disaggregated LLM Serving
Shrikara Arun, Anjaly Parayil, Srikant Bharadwaj, Renee St. Amant, Victor Rühle
cs.DC · cs.AI
Disaggregated LLM serving runs prefill and decode on separate GPU pools to keep the two phases from interfering. In practice, this creates a new asymmetry: under bursty, heavy-tailed workloads prefill nodes saturate while decode nodes have compute underutilized, and on a production-style A100 cluster with 2 prefill and 2 decode nodes (2P2D), we find that prefill execution accounts for only 2-23% of P95 Time-to-First-Token (TTFT). Queuing and...
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07
Mixture-of-Parallelisms: Towards Memory-Efficient Training Stack for Mixture-of-Experts Models
Xuan-Phi Nguyen, Shrey Pandit, Yiran Zhao, Semih Yavuz, Silvio Savarese, Shafiq Joty
cs.DC · cs.AI
This paper showcases a memory-efficient training stack for Mixture-of-Experts (MoE) models. It is a training paradigm that combines and specializes various existing and novel parallelism techniques at different layers and stages of the Mixture-of-Experts (MoE) model training pipeline. It leverages these techniques to achieve maximal efficiency given the physical constraints of CPU, CPU memory, GPU HBM memory, and the CPU-GPU, GPU-GPU, and...
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08
Lynx: Progressive Speculative Quantization for accelerating KV Transfer in Long-Context Inference
Wenchen Han, Gingfung Matthew Yeung, Marco Barletta, William Toner, Amory Hoste, Adam Barker
cs.DC · cs.LG
Long-context inference is increasingly common in large language model (LLM) serving, driven by retrieval-augmented generation and agentic systems. In disaggregated inference, these workloads require transferring large Key-Value (KV) caches across the network, where decoding cannot begin until the transfer completes. Recent KV quantization techniques reduce data volume and alleviate this bottleneck, but existing schemes fail to achieve both...
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09
HCMS: Head-Chunked Multi-Stream Pipeline for Communication-Computation Overlap in Long-Sequence Parallel Attention
Chao Yuan, Pan Li, Yingnan Sun, Jing Liu
cs.DC
All-to-all based sequence parallelism methods execute communication and computation strictly in serial when processing medium-long sequences, resulting in hardware resource underutilization. This paper proposes Head-Chunked Multi-Stream Pipeline (HCMS), which exploits the computational independence of multi-head attention by partitioning attention heads into multiple chunks and achieving fine-grained communication-computation overlap through...
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