11 research outputs found
Predictive runtime code scheduling for heterogeneous architectures
Heterogeneous architectures are currently widespread. With
the advent of easy-to-program general purpose GPUs, virtually every re-
cent desktop computer is a heterogeneous system. Combining the CPU
and the GPU brings great amounts of processing power. However, such
architectures are often used in a restricted way for domain-speci c appli-
cations like scienti c applications and games, and they tend to be used
by a single application at a time. We envision future heterogeneous com-
puting systems where all their heterogeneous resources are continuously
utilized by di erent applications with versioned critical parts to be able
to better adapt their behavior and improve execution time, power con-
sumption, response time and other constraints at runtime. Under such a
model, adaptive scheduling becomes a critical component.
In this paper, we propose a novel predictive user-level scheduler based on
past performance history for heterogeneous systems. We developed sev-
eral scheduling policies and present the study of their impact on system
performance. We demonstrate that such scheduler allows multiple appli-
cations to fully utilize all available processing resources in CPU/GPU-
like systems and consistently achieve speedups ranging from 30% to 40%
compared to just using the GPU in a single application mode.Postprint (published version
The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
Graph data management is instrumental for several use cases
such as recommendation, root cause analysis, financial fraud detection,
and enterprise knowledge representation. Efficiently supporting these use
cases yields a number of unique requirements, including the need for a
concise query language and graph-aware query optimization techniques.
The goal of the Linked Data Benchmark Council (LDBC) is to design
a set of standard benchmarks that capture representative categories of
graph data management problems, making the performance of systems
comparable and facilitating competition among vendors. LDBC also
conducts research on graph schemas and graph query languages. This
paper introduces the LDBC organization and its work over the last decade
Knowledge-guided docking of WW domain proteins and flexible ligands
10.1007/978-3-642-04031-3_16Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5780 LNBI175-18