VLDB 2026 Research / reviewers in the wild / expert
Sree Charan Gundabolu
dblp:290/7572
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0001-6220-6926ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 first-author · 2 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.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
GPUs and heterogeneous computing · 50% Parallel and multicore computing · 32% High-performance computing · 18% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
sequence alignment |
1.4 | 2 | 2025 | BLAZE: Exploiting Hybrid Parallelism and Size-customized Kernels to Accelerate BLASTP on GPUs · SC 2025 FastZ: accelerating gapped whole genome alignment on GPUs · SC 2021 |
GPUs and heterogeneous computing
GPU computing |
1.4 | 2 | 2025 | BLAZE: Exploiting Hybrid Parallelism and Size-customized Kernels to Accelerate BLASTP on GPUs · SC 2025 FastZ: accelerating gapped whole genome alignment on GPUs · SC 2021 |
Bioinformatics and computational biology › sequence alignment
protein sequence alignment |
0.9 | 1 | 2025 | BLAZE: Exploiting Hybrid Parallelism and Size-customized Kernels to Accelerate BLASTP on GPUs · SC 2025 |
Parallel and multicore computing › parallelization strategies
hybrid parallelism |
0.9 | 1 | 2025 | BLAZE: Exploiting Hybrid Parallelism and Size-customized Kernels to Accelerate BLASTP on GPUs · SC 2025 |
Bioinformatics and computational biology › sequence alignment › genome alignment
whole-genome alignment |
0.5 | 1 | 2021 | FastZ: accelerating gapped whole genome alignment on GPUs · SC 2021 |
High-performance computing
performance optimization at scale |
0.5 | 1 | 2021 | FastZ: accelerating gapped whole genome alignment on GPUs · SC 2021 |
Methods — techniques the papers use, named apart from their topics
size-customized kernels · 1.7register-based cyclic-buffering · 1.0load balancing · 1.0inspector-executor scheme · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | BLAZE: Exploiting Hybrid Parallelism and Size-customized Kernels to Accelerate BLASTP on GPUsabstractBasic Local Alignment Search Tool (BLAST/BLASTP) [3], often referred to as the Google of biological research [9], is widely used to query a large database to find homologous gene/protein sequences. Though there have been attempts to accelerate BLASTP, the protein sequence alignment tool, on GPUs, they remain slower than CPU/multicore-based multi-threaded implementations. In this paper, we introduce BLAZE, a GPU-accelerated drop-in replacement for BLASTP that produces identical results while achieving speedups over both multithreaded and GPU-accelerated implementations. Sree Charan Gundabolu, Mithuna Thottethodi, T. N. Vijaykumar |
SC | 1 |
| 2021 | FastZ: accelerating gapped whole genome alignment on GPUsabstractRecognizing the importance of whole genome alignment (WGA), the National Institutes for Health maintains LASTZ, a sequential WGA application. As genomic data grows, there is a compelling need for scalable, high-performance WGA. Unfortunately, high-sensitivity, `gapped' alignment which uses dynamic programming (DP) is slow, whereas faster alignment with ungapped filtering is often less sensitive. We develop FastZ, a GPU-accelerated, gapped WGA software which matches gapped LASTZ in sensitivity. FastZ employs a novel inspector-executor scheme in which (a) the lightweight inspector elides DP traceback except in common, extremely short alignments, where the inspector performs limited, eager traceback to eliminate the executor, and (b) executor trimming avoids unnecessary work. Further, FastZ employs register-based cyclic-buffering to drastically reduce memory traffic, and groups DP problems by size for load balance. FastZ running on an RTX 3080 GPU and our multicore implementation of LASTZ achieve 111x and 20x speedups over the sequential LASTZ, respectively. Sree Charan Gundabolu, T. N. Vijaykumar, Mithuna Thottethodi |
SC | 1 |