131 papers
This paper presents the design, analysis, and performance evaluation of a concurrent deterministic skiplist alongside lock-free queue and hash table implementations on many-core NUMA systems, introducing memory management strategies and hierarchical usage patterns to reduce page faults, cache misses, and remote memory access latencies.
This paper proposes a hybrid methodology combining theoretical modeling with empirical benchmarking to accurately determine the optimal allocation of Prefill-Decode disaggregated hardware resources for Large Language Model inference while satisfying throughput, SLO, and request characteristic constraints.
OSGym is a scalable, cost-effective, and generalizable distributed data engine that enables the efficient training of computer agents by parallelizing over a thousand OS replicas to generate vast amounts of diverse interaction data for supervised fine-tuning and reinforcement learning.
This paper introduces Data Center Execution Assurance (DCEA), a cryptographic framework that binds Confidential VM attestations to platform-level TPM evidence to provide verifiable proof that workloads are executing within a trusted data center, thereby preventing proxy and relay attacks that existing mechanisms cannot detect.
This paper proposes a forecast-based multi-horizon optimization approach that reduces the carbon footprint of energy-intensive, location-constrained services like generative AI by dynamically adjusting response quality tiers in response to grid carbon intensity, achieving up to 10% emission reductions while adhering to annual carbon budgets.
PTOPOFL is a privacy-preserving personalized federated learning framework that replaces gradient sharing with compact persistent homology descriptors to simultaneously mitigate data-reconstruction risks and improve aggregation performance on non-IID data through topology-guided clustering and weighted aggregation.
The paper proposes SENTINEL, a lightweight, momentum-based verification mechanism that ensures the integrity of pipeline parallel decentralized training across untrusted nodes by detecting corrupted inter-stage communications without computation duplication, thereby enabling the secure training of large-scale models like 4B-parameter LLMs.
This paper identifies that LLM-based agents systematically fail at cloud root cause analysis due to architectural flaws like hallucinated data interpretation and incomplete exploration rather than model limitations, demonstrating that while prompt engineering is insufficient, enhancing inter-agent communication protocols can significantly reduce specific failure modes.
This paper proposes a time-aware beam search heuristic that efficiently partitions quantum circuits for distributed computing by incrementally optimizing qubit assignments across time steps to minimize communication overhead, outperforming static and metaheuristic baselines with superior scalability.
This paper explores the use of multipartite entanglement resources and four-dimensional qudits to efficiently implement distributed fan-out operations and global Mølmer-Sørensen gates, offering insights for future quantum circuit compilation and data center design.
This paper proposes a memory-efficient classical simulation method for quantum machine learning that fuses consecutive gates in both forward and backward paths to minimize global memory access, achieving up to 30 times higher throughput and enabling the training of large-scale deep quantum circuits on consumer GPUs.