EDBT 2026 Demo / reviewers in the wild / expert
Arnt Emil Ingulstad
dblp:430/6302
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0009-1880-2451ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Memory systems · 61% Storage systems · 30% Cloud and datacenter computing · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › memory disaggregation
CXL memory |
1.0 | 1 | 2026 | No Atomics, No Problem. Developing a RAG Pipeline for Shared CXL Memory · IEEE Trans. Computers 2026 |
Storage systems
file systems |
1.0 | 1 | 2026 | No Atomics, No Problem. Developing a RAG Pipeline for Shared CXL Memory · IEEE Trans. Computers 2026 |
Memory systems
shared memory |
1.0 | 1 | 2026 | No Atomics, No Problem. Developing a RAG Pipeline for Shared CXL Memory · IEEE Trans. Computers 2026 |
Cloud and datacenter computing › cluster resource management and scheduling
cluster resource management |
0.3 | 1 | 2026 | No Atomics, No Problem. Developing a RAG Pipeline for Shared CXL Memory · IEEE Trans. Computers 2026 |
Methods — techniques the papers use, named apart from their topics
vector similarity search · 1.0retrieval-augmented generation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | No Atomics, No Problem. Developing a RAG Pipeline for Shared CXL MemoryabstractWe share early experiments with software development for shared CXL memory on the H3 Falcon C5022 CXL switch. We describe how the different stages in the development of a commercial RAG pipeline were impacted by the presence of CXL memory. All of Wikipedia is split into 46 million text passages which are embedded into a high-dimensional embedding space and subjected to heavy load in single-and multi-host experiments. We observe significant performance advantages available to DuckDB and Faiss without changing the software; we measure up to 12× latency reduction with real workloads and 40× reduction with synthetic workloads on CXL, compared to the same queries run with demand paging on NVMe. We explain the current challenges of working with two disjoint cache coherency domains and explain how the Fabric-Attached Memory File System (famfs) and famfs producer-consumer queues provide synchronization patterns without atomic operations on current ×86 CPUs. We then discuss open challenges remaining for high performance atomics and locking mechanisms in shared memory. Lastly we show how famfs page-level interleaving enables near-linear throughput scaling when a second host serves queries from the same Faiss index on shared fabric-attached memory and how shared memory allocations can be orchestrated by Kubernetes in a full vertical RAG deployment. Alfred Bratterud, Gisle Dankel, Amin Farajianzadeh, John Groves, Chengyi Juan, Joshua Suetterlein, Andrés Márquez 0001, Petter Gustad, Arnt Emil Ingulstad |
IEEE Trans. Computers | 9 |