Demo Data from EDBT 2026 / reviewers in the wild / expert
Luochangqi Ding
dblp:314/7083
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2026
0000-0003-4927-4398ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 since 2021Computer networks · 1 · 1 since 2021Software 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
3 papers |
Storage systems · 40% Memory systems · 36% Parallel and multicore computing · 16% | |
| Computer networks
1 paper |
Datacenter networks · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Concurrent programming · 77% Program analysis · 23% |
Topics — the 11 heaviest of 11, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › non-volatile memory
persistent memory |
1.2 | 2 | 2023 | Lock-Free High-performance Hashing for Persistent Memory via PM-aware Holistic Optimization · ACM Trans. Archit. Code Optim. 2023 Efficiently detecting concurrency bugs in persistent memory programs · ASPLOS 2022 |
Datacenter networks
RDMA |
1.0 | 1 | 2026 | BURST: Seeking High-performance, Interoperability and Scalability in Soft-RDMA · NSDI 2026 |
Parallel and multicore computing › concurrent data structures
concurrent hashtable |
0.7 | 1 | 2023 | Lock-Free High-performance Hashing for Persistent Memory via PM-aware Holistic Optimization · ACM Trans. Archit. Code Optim. 2023 |
Storage systems
key-value storage |
0.7 | 1 | 2023 | Lock-Free High-performance Hashing for Persistent Memory via PM-aware Holistic Optimization · ACM Trans. Archit. Code Optim. 2023 |
Concurrent programming
concurrency bugs |
0.6 | 1 | 2022 | Efficiently detecting concurrency bugs in persistent memory programs · ASPLOS 2022 |
Storage systems › crash consistency
crash-consistency bug detection |
0.6 | 1 | 2022 | Efficiently detecting concurrency bugs in persistent memory programs · ASPLOS 2022 |
Interconnection networks and networks-on-chip
interconnection networks |
0.3 | 1 | 2026 | BURST: Seeking High-performance, Interoperability and Scalability in Soft-RDMA · NSDI 2026 |
Storage systems
crash consistency |
0.2 | 1 | 2023 | Lock-Free High-performance Hashing for Persistent Memory via PM-aware Holistic Optimization · ACM Trans. Archit. Code Optim. 2023 |
Memory systems
non-volatile memory |
0.2 | 1 | 2023 | Lock-Free High-performance Hashing for Persistent Memory via PM-aware Holistic Optimization · ACM Trans. Archit. Code Optim. 2023 |
Storage systems
storage reliability |
0.2 | 1 | 2023 | Lock-Free High-performance Hashing for Persistent Memory via PM-aware Holistic Optimization · ACM Trans. Archit. Code Optim. 2023 |
Program analysis › static analysis
bug detection |
0.2 | 1 | 2022 | Efficiently detecting concurrency bugs in persistent memory programs · ASPLOS 2022 |
Methods — techniques the papers use, named apart from their topics
dynamic analysis · 1.1concurrency bug detection · 1.1lock-free concurrency · 0.7atomic primitives · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | BURST: Seeking High-performance, Interoperability and Scalability in Soft-RDMA
Huijun Shen, Zelong Yue, Zhuo Jiang, Lang An, Luochangqi Ding, Xiaolong Zhong, Jianxi Ye, Xijin Yin, Xingyu Guo |
NSDI | 8 |
| 2023 | Lock-Free High-performance Hashing for Persistent Memory via PM-aware Holistic OptimizationabstractPersistent memory (PM) provides large-scale non-volatile memory (NVM) with DRAM-comparable performance. The non-volatility and other unique characteristics of PM architecture bring new opportunities and challenges for the efficient storage system design. For example, some recent crash-consistent and write-friendly hashing schemes are proposed to provide fast queries for PM systems. However, existing PM hashing indexes suffer from the concurrency bottleneck due to the blocking resizing and expensive lock-based concurrency control for queries. Moreover, the lack of PM awareness and systematical design further increases the query latency. To address the concurrency bottleneck of lock contention in PM hashing, we propose clevel hashing, a lock-free concurrent level hashing scheme that provides non-blocking resizing via background threads and lock-free search/insertion/update/deletion using atomic primitives to enable high concurrency for PM hashing. By exploiting the PM characteristics, we present a holistic approach to building clevel hashing for high throughput and low tail latency via the PM-aware index/allocator co-design. The proposed volatile announcement array with a helping mechanism coordinates lock-free insertions and guarantees a strong consistency model. Our experiments using real-world YCSB workloads on Intel Optane DC PMM show that clevel hashing, respectively, achieves up to 5.7× and 1.6× higher throughput than state-of-the-art P-CLHT and Dash while guaranteeing low tail latency, e.g., 1.9×–7.2× speedup for the p99 latency with the insert-only workload. Zhangyu Chen, Yu Hua 0001, Luochangqi Ding, Bo Ding 0002, Pengfei Zuo, Xue (Steve) Liu |
ACM Trans. Archit. Code Optim. | 3 |
| 2022 | Efficiently detecting concurrency bugs in persistent memory programsabstractDue to the salient DRAM-comparable performance, TB-scale capacity, and non-volatility, persistent memory (PM) provides new opportunities for large-scale in-memory computing with instant crash recovery. However, programming PM systems is error-prone due to the existence of crash-consistency bugs, which are challenging to diagnose especially with concurrent programming widely adopted in PM applications to exploit hardware parallelism. Existing bug detection tools for DRAM-based concurrency issues cannot detect PM crash-consistency bugs because they are oblivious to PM operations and PM consistency. On the other hand, existing PM-specific debugging tools only focus on sequential PM programs and cannot effectively detect crash-consistency issues hidden in concurrent executions. Zhangyu Chen, Yu Hua 0001, Yongle Zhang 0007, Luochangqi Ding |
ASPLOS | 4 |