Demo Data from VLDB 2026 / reviewers in the wild / expert
Vladimir Kotlyar
dblp:92/4614
· DBLP profile ↗
7ranked-venue papers
4as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 4 · 3 first-authorDatabases, data management, data science and information retrieval · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1
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.
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization › code generation
parallel code generation |
0.0 | 1 | 1997 | Compiling Parallel Code for Sparse Matrix Applications · SC 1997 |
Methods — techniques the papers use, named apart from their topics
relational algebra · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Signal Processing Algorithms For Dsp Implementation of Analog TV ReceiversabstractThis paper presents a novel power efficient software implementation of the NTSC analog TV receiver using the Sandbridge Technologies software defined radio (SDR) platform. The radio frequency signal specific to a TV channel is down converted to base band and sampled at 14.318 MHz. From this stage, the entire receiver function is executed in SW, including the horizontal and vertical synchronization, and video processing. The analog TV receiver implemented in DSP can coexist with other communication protocols such as WLAN, GSM/GPRS, GPS etc. allowing analog TV broadcasting on mobile platforms Hua Ye 0003, Daniel Iancu, C. John Glossner, Vladimir Kotlyar, Andrei Iancu |
ICASSP (3) | 4 |
| 2001 | Personalization of Supermarket Product Recommendations
Richard D. Lawrence, George S. Almási, Vladimir Kotlyar, Marisa S. Viveros, Sastry Duri |
Data Min. Knowl. Discov. | 3 |
| 2000 | Next-generation generic programming and its application to sparse matrix computationsabstractThe contributions of this paper are the following. Nikolay Mateev, Keshav Pingali, Paul Stodghill, Vladimir Kotlyar |
ICS | 4 |
| 1999 | A Case Study in Information Delivery to Mass Retail Markets
Vladimir Kotlyar, Marisa S. Viveros, Sastry Duri, Richard D. Lawrence, George S. Almási |
DEXA | 1 |
| 1997 | A Relational Approach to the Compilation of Sparse Matrix Programs
Vladimir Kotlyar, Keshav Pingali, Paul Stodghill |
Euro-Par | 1 |
| 1997 | Sparse Code Generation for Imperfectly Nested Loops with DependencesabstractStandard restructuring compiler tools are based on polyhedral algebra and cannot be used to analyze or restructure sparse matrix codes. We have recently shown that tools based on relational algebra can be used to generate an efficient sparse matrix program from the corresponding dense matrix program and a specification of the sparse matrix format. This work was restricted to DO-ALL loops and loops with reductions. In this paper, we extend this approach to loops with dependences. Although our results are restricted to Compressed Hyperplane Storage formats, they apply to both perfectly nested loops and imperfectly nested loops. 1 INTRODUCTION Although sparse matrix computations are ubiquitous in computational science, research in restructuring compilers has focused almost exclusively on dense matrix programs. This is because the tools used in restructuring compilers are based on the algebra of polyhedra, and can be used only when array subscripts are affine functions of loop index vari... Vladimir Kotlyar, Keshav Pingali |
International Conference on Supercomputing | 1 |
| 1997 | Compiling Parallel Code for Sparse Matrix ApplicationsabstractWe have developed a framework based on relational algebra for compiling efficient sparse matrix code from dense DO-ANY loops and a specification of the representation of the sparse matrix. In this paper, we show how this framework can be used to generate parallel code, and present experimental data that demonstrates that the code generated by our Bernoulli compiler achieves performance competitive with that of hand-written codes for important computational kernels. Vladimir Kotlyar, Keshav Pingali, Paul Stodghill |
SC | 1 |