VLDB 2026 Research / reviewers in the wild / expert
Kalyan Subramanian
dblp:408/0093
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0002-5144-4405ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 3 since 2021Software engineering, systems software and programming languages · 3 · 3 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Power Sloshing in Compound Servers for Large-Scale AI Inference Workloads
Albert Cho, Jovan Stojkovic, Leonardo Piga, Abhishek Dhanotia, Sultan Mahmud Sajal, Gefei Zuo, Krishna T. Malladi, Devon Akers, Kalyan Subramanian, Shobhit O. Kanaujia, Alexandros Daglis |
ISCA | 9 |
| 2026 | Vistara: Making CXL Real-Full Path From ASIC Design and OS Support to Hyperscale Deployment
Neha Gholkar, Jovan Stojkovic, Hasan Al Maruf, Gregory Price, Prakash Chauhan, Hiral Patel, Cedric Van Goethem Kiran Vemuri, Kiran Malwankar, Kishore Sriadibhatla, Kalyan Subramanian, Shobhit O. Kanaujia, Chunqiang Tang, Abhishek Dhanotia |
ISCA | 10 |
| 2025 | DCPerf: An Open-Source, Battle-Tested Performance Benchmark Suite for Datacenter WorkloadsabstractWe present DCPerf, the first open-source performance benchmark suite actively used to inform procurement decisions for millions of CPU in hyperscale datacenters.Although numerous benchmarks exist, our evaluation reveals that they inaccurately project server performance for datacenter workloads or fail to scale to resemble production workloads on modern many-core servers.DCPerf distinguishes itself in two aspects: (1) it faithfully models essential software architectures and features of datacenter applications, such as microservice architecture and highly optimized multi-process or multi-thread concurrency; and (2) it strives to align its performance characteristics with those of production workloads, at both the system level and microarchitecture level.Both are made possible by our direct access to the source code and hyperscale production deployments of datacenter workloads.Additionally, we share real-world examples of using DCPerf in critical decision-making, such as selecting future CPU SKUs and guiding CPU vendors in optimizing their designs.Our evaluation demonstrates that DCPerf accurately projects the performance of representative production workloads within a 3.3% error margin across four generations of production servers introduced over a span of six years, with core counts varying widely from 36 to 176. Wei Su 0005, Abhishek Dhanotia, Jayneel Gandhi, Neha Gholkar, Shobhit O. Kanaujia, Maxim Naumov, Kalyan Subramanian, Valentin Andrei, Chunqiang Tang |
ISCA | 8 |