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Abstract: Broadcasting and aggregation dominate the communication overhead in distributed systems, from machine learning training to data analytics. Current acceleration approaches require specialized hardware (RDMA) or dedicated resources (DPDK), limiting their deployment in commodity clouds. However, we present a counter-intuitive alternative: rather than bypassing the kernel, we move operations into it using eBPF. While this imposes severe constraints including no floating-point, limited memory, and stateless execution, we show these restrictions paradoxically drive innovative protocol designs that yield unexpected benefits. We introduce AggBox, which implements broadcast and aggregation operations entirely within eBPF’s constrained environment. Our key innovations include stateless group acknowledgments for reliability, edge quantization for floating-point aggregation using only integer arithmetic, and tail-call chains that create virtual memory beyond eBPF’s 512-byte stack limit. These designs emerge from and exploit the constraints rather than fighting them. AggBox achieves remarkable performance on commodity hardware: 84.5% reduction in broadcast latency, 43× speedup for MapReduce workloads, and 56.1% faster ML gradient aggregation, all without specialized NICs or dedicated cores. Beyond performance, our work demonstrates that constrained environments can drive fundamental innovation in protocol design, offering insights for future resource-limited and verified systems. 

Abstract Understanding how the 3D structure of the genome influences gene regulation is a growing area of interest, particularly in the context of alternative post-transcriptional regulatory events such as alternative splicing (AS) and alternative polyadenylation (APA). These processes are essential for generating transcript and protein diversity, and they are tightly coordinated with transcription. However, despite their biological importance, the relationship between chromatin interactions and alternative pre-messenger RNA regulation remains poorly understood. This gap largely stems from a lack of computational tools capable of integrating structural genomic data with RNA processing dynamics. Exploring how chromatin interactions and epigenetic landscapes shape these events is essential for uncovering the multilayered regulation of gene expression. To bridge this gap, we present EpGAT, a graph attention network-based model that integrates epigenetic read coverage and chromatin interaction data to predict and quantify AS and APA events. By explicitly modeling the spatial organization of the genome, EpGAT captures the regulatory influence of chromatin looping and long-range genomic interactions on RNA processing. The model’s predictions are validated through rigorous cross-cell line and cross-chromosome evaluations, affirming its generalizability and reliability. Beyond prediction, EpGAT offers interpretability by tracing learned parameters back to genomic features, enabling the identification of active enhancers, mapping promoter–enhancer connectivity, and pinpointing the epigenetic factors most critical to specific RNA processing events. These capabilities make EpGAT a powerful tool for dissecting the complex interplay between genome architecture and transcriptomic regulation. More broadly, it provides a generalizable framework for multiple tasks to study the link between 3D genome organization, epigenetic signals, and RNA processing.