Polynomial Histograms for Memory-Efficient Representation of Long-tailed System Distributions

2026-06-01T08:52:29Z45949607954e74599e867eaa307750ce4cb3868e4b7a5ef5060fea56a9677169
CRDTCUDA-GraphsDatalogGEMMGPU-performanceInvariant-Bit-PackingLLM-inferencebatch-1distributed-systemsenergy-efficiencyevolutionary-searchfederated-learning','personalization','gumbel-softmax','arxiv','histogramskernel-optimizationlong-tailed-distributionslossless-compressionmemory-bandwidthparallelizationperformance-ruggednessradio-networksschedulingspanning-treetelemetryvirtual-processorwireless-sensor-actuator-networks

What happened

Collection of recent arXiv CS papers (distributed systems, ML systems, and high-performance computing). Highlights: polynomial histograms for compact, low-loss representation of long-tailed telemetry; randomized energy-efficient aggregation and near-optimal minimum-degree spanning-tree construction for radio networks; a Datalog-based declarative framework for specifying and testing CRDT semantics; performance-ruggedness analysis for GEMM with software mitigations that reduce variance and raise throughput; Kernel Foundry, an evolutionary, diagnosis-driven GPU-kernel optimizer with a reusable经验/

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_dc
Record identifier
45949607954e74599e867eaa307750ce4cb3868e4b7a5ef5060fea56a9677169
Enrichment time
2026-06-01T08:52:29Z
AI-assisted enrichment
Yes

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