5 citations · 17 across the 13 of their papers we have counts for
7 papers · 1 filter
EDAN: Towards Understanding Memory Parallelism and Latency Sensitivity in HPC
Siyuan Shen, Mikhail Khalilov, Lukas Gianinazzi +6
Resource disaggregation is a promising technique for improving the efficiency of large-scale computing systems. However, this comes at the cost of increased memory access latency d…
PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
Andrei Ivanov, Siyuan Shen, Gioele Gottardo +5
The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a signifi…
Hazel: Secure and Efficient Disaggregated Storage
Marcin Chrapek, Meni Orenbach, Ahmad Atamli +4
Disaggregated storage with NVMe-over-Fabrics (NVMe-oF) has emerged as the standard solution in modern supercomputers and data center clusters, achieving superior performance, resou…
Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
Marcin Chrapek, Marcin Copik, Etienne Mettaz +1
Large Language Models (LLMs) are increasingly deployed on converged Cloud and High-Performance Computing (HPC) infrastructure. However, as LLMs handle confidential inputs and are f…
Psychologically Enhanced AI Agents
Maciej Besta, Shriram Chandran, Robert Gerstenberger +9
We introduce MBTI-in-Thoughts, a framework for enhancing the effectiveness of Large Language Model (LLM) agents through psychologically grounded personality conditioning. Drawing o…
Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors
Patrick Iff, Paul Bruegger, Marcin Chrapek +3
Advances in embedding models for text, image, audio, and video drive progress across multiple domains, including retrieval-augmented generation, recommendation systems, and others.…
SDR-RDMA: Software-Defined Reliability Architecture for Planetary Scale RDMA Communication
Mikhail Khalilov, Siyuan Shen, Marcin Chrapek +16
RDMA is vital for efficient distributed training across datacenters, but millisecond-scale latencies complicate the design of its reliability layer. We show that depending on long-…