EDBT 2026 Demo / reviewers in the wild / expert
Santosh Sonawane
dblp:81/11210
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 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 |
Performance modeling and evaluation · 56% Cloud and datacenter computing · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing
cluster resource management and scheduling |
0.8 | 1 | 2024 | ServiceLab: Preventing Tiny Performance Regressions at Hyperscale through Pre-Production Testing · OSDI 2024 |
Performance modeling and evaluation › benchmarking
performance regression testing |
0.8 | 1 | 2024 | ServiceLab: Preventing Tiny Performance Regressions at Hyperscale through Pre-Production Testing · OSDI 2024 |
Performance modeling and evaluation
benchmarking |
0.2 | 1 | 2024 | ServiceLab: Preventing Tiny Performance Regressions at Hyperscale through Pre-Production Testing · OSDI 2024 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | ServiceLab: Preventing Tiny Performance Regressions at Hyperscale through Pre-Production Testing
Mike Chow, Yang Wang 0009, Ayichew Hailu, Rohan Bopardikar, Jialiang Qu, David Meisner, Santosh Sonawane, Rodrigo Paim, Mack Ward, Ivor Huang, Matt McNally, Daniel Hodges, Zoltan Farkas, Caner Gocmen, Elvis Huang, Chunqiang Tang |
OSDI | 9 |
| 2011 | Optimization of an Instrumentation Tool for Stripped Win32/X86 BinariesabstractMany software security, instruction set architecture virtualization and performance enhancement techniques require instrumentation of application program binaries either to add run-time checks or to perform dynamic analysis and transformation. Unfortunately, commercially distributed application binaries on the Win32 platform are often stripped of their symbol table, and therefore cannot be easily disassembled, let alone correctly instrumented. BIRD is an instrumentation tool that applies an IA-32 disassembler both statically and dynamically, and successfully guarantees that no instruction in an input binary can be executed without being examined first. Unfortunately, the first version of BIRD has several performance problems. This paper describes our experiences of optimizing the first BIRD prototype to remove these problems. In particular, we develop a novel speculative disassembly technique that successfully reaps most of the performance benefits of static disassembly while ensuring the same level of correctness as dynamic disassembly, a bitmap-based target address check algorithm that reduces the fixed performance overhead associated with every instrumentation, and a comprehensive in-place instrumentation technique that relies mostly on instruction substitution and drastically cuts down the number of debug exceptions (int 3) invoked at run time. Together these performance optimizations reduce the average performance overhead of a set of batch Win32 programs from 23.6% to 8.8%. Santosh Sonawane, Tzi-cker Chiueh |
ICPADS | 1 |