10 citations · 14 across the 12 of their papers we have counts for
13 papers
Dissecting GPU Utilization for LLM Inference on Nvidia Hopper
Mohammad Siavashi, Gerald Q. Maguire, Dejan Kostic +1
A single SM utilization percentage can make an LLM inference workload look compute-saturated while hiding how much useful work is being done. The problem is not that the counter is…
Avoiding Cross-Datacenter Collective Congestion via Disaggregated Buffering
Mariano Scazzariello, Noga H. Rotman, Dima Gavrilenko +6
LLM training at the scale of tens of thousands of GPUs now spans multiple datacenters (DC), making cross-DC collectives over long-haul links unavoidable. A critical and overlooked…
Blink: CPU-Free LLM Inference by Delegating the Serving Stack to GPU and SmartNIC
Mohammad Siavashi, Mariano Scazzariello, Gerald Q. Maguire +2
Large Language Model (LLM) inference is rapidly becoming a core datacenter service, yet current serving stacks keep the host CPU on the critical path for orchestration and token-le…
Dissect-and-Restore: AI-based Code Verification with Transient Refactoring
Changjie Wang, Mariano Scazzariello, Anoud Alshnakat +3
Formal verification is increasingly recognized as a critical foundation for building reliable software systems. However, the need for specialized expertise to write precise specifi…
KVComm: Enabling Efficient LLM Communication through Selective KV Sharing
Xiangyu Shi, Marco Chiesa, Gerald Q. Maguire +1
Large Language Models (LLMs) are increasingly deployed in multi-agent systems, where effective inter-model communication is crucial. Existing communication protocols either rely on…
Can LLMs Forecast Internet Traffic from Social Media?
Jonatan Langlet, Mariano Scazzariello, Flavio Luciani +3
Societal events shape the Internet's behavior. The death of a prominent public figure, a software launch, or a major sports match can trigger sudden demand surges that overwhelm pe…
Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference
Mohammad Siavashi, Faezeh Keshmiri Dindarloo, Dejan Kostic +1
Large Language Models have revolutionized natural language processing, yet serving them efficiently in data centers remains challenging due to mixed workloads comprising latency-se…
Automating the Detection of Code Vulnerabilities by Analyzing GitHub Issues
Daniele Cipollone, Changjie Wang, Mariano Scazzariello +4
In today's digital landscape, the importance of timely and accurate vulnerability detection has significantly increased. This paper presents a novel approach that leverages transfo…
From Scientific Texts to Verifiable Code: Automating the Process with Transformers
Changjie Wang, Mariano Scazzariello, Marco Chiesa
Despite the vast body of research literature proposing algorithms with formal guarantees, the amount of verifiable code in today's systems remains minimal. This discrepancy stems f…
Deriving Coding-Specific Sub-Models from LLMs using Resource-Efficient Pruning
Laura Puccioni, Alireza Farshin, Mariano Scazzariello +3
Large Language Models (LLMs) have demonstrated their exceptional performance in various complex code generation tasks. However, their broader adoption is limited by significant com…
Just-in-Time Packet State Prefetching
Hamid Ghasemirahni, Alireza Farshin, Dejan Kostic +1
Could information about future incoming packets be used to build more efficient CPU-based packet processors? Can such information be obtained accurately? This paper studies novel p…
FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
Giacomo Verardo, Magnus Boman, Samuel Bruchfeld +4
Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various Aut…
Making Network Configuration Human Friendly
Changjie Wang, Mariano Scazzariello, Alireza Farshin +2
This paper explores opportunities to utilize Large Language Models (LLMs) to make network configuration human-friendly, simplifying the configuration of network devices and minimiz…