Demo Data from EDBT 2026 / reviewers in the wild / expert
Sophia Fu
dblp:417/7305
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0002-2358-3336ORCID · 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 |
Processor architecture and microarchitecture · 87% High-performance computing · 13% | |
| Network and information security
1 paper |
Cryptographic primitives and cryptanalysis · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic primitives and cryptanalysis › finite field arithmetic
number theoretic transform |
0.9 | 1 | 2025 | Towards Closing the Performance Gap for Cryptographic Kernels Between CPUs and Specialized Hardware · MICRO 2025 |
Processor architecture and microarchitecture › instruction set architecture
instruction set extension |
0.9 | 1 | 2025 | Towards Closing the Performance Gap for Cryptographic Kernels Between CPUs and Specialized Hardware · MICRO 2025 |
Processor architecture and microarchitecture › SIMD
SIMD extensions |
0.9 | 1 | 2025 | Towards Closing the Performance Gap for Cryptographic Kernels Between CPUs and Specialized Hardware · MICRO 2025 |
Methods — techniques the papers use, named apart from their topics
roofline analysis · 1.7AVX2 · 1.7AVX512 · 0.9AVX-512 · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Towards Closing the Performance Gap for Cryptographic Kernels Between CPUs and Specialized HardwareabstractSpecialized hardware like application-specific integrated circuits (ASICs) remains the primary accelerator type for cryptographic kernels based on large integer arithmetic.Prior work has shown that commodity and server-class GPUs can achieve near-ASIC performance for these workloads.However, achieving comparable performance on CPUs remains an open challenge.This work investigates the following question: How can we narrow the performance gap between CPUs and specialized hardware for key cryptographic kernels like basic linear algebra subprograms (BLAS) operations and the number theoretic transform (NTT)?To this end, we develop an optimized scalar implementation of these kernels for x86 CPUs at the per-core level.We utilize SIMD instructions-specifically AVX2 and AVX-512-to further improve performance, achieving an average speedup of 38 times and 62 times over state-of-the-art CPU baselines for NTTs and BLAS operations, respectively.To narrow the gap further, we propose a small AVX-512 extension, dubbed multi-word extension (MQX), which delivers substantial speedup with only three new instructions and minimal proposed hardware modifications.MQX cuts the slowdown relative to ASICs to as low as 35 times on a single CPU core.Finally, we perform a roofline analysis to evaluate the peak performance achievable with MQX when scaled across an entire multi-core CPU.Our results show that, with MQX, top-tier servergrade CPUs can approach the performance of state-of-the-art ASICs for cryptographic workloads. Naifeng Zhang, Sophia Fu, Franz Franchetti |
MICRO | 2 |