8 papers
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.…
Multi-Head RAG: Solving Multi-Aspect Problems with LLMs
Maciej Besta, Ales Kubicek, Robert Gerstenberger +13
Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by retrieving supporting documents into the prompt, but existing methods do not explicitly target queries…
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…
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…
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…