In recent years, the heterogeneity of both commodity and supercomputers hardware has
increased sharply. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often …

Autotuning is an important method for automatically exploring code optimizations. It may
target low-level code optimizations, such as memory blocking, loop unrolling or memory …

Due to the complexity and diversity of new parallel architectures automatic tuning of parallel
applications has become increasingly important for achieving acceptable performance …

The ability to understand how a scientific application is executed on a large HPC system is
of great importance in allocating resources within the HPC data center. In this paper, we …

The efficient parallel execution of scientific applications is a key challenge in high-
performance computing (HPC). With growing parallelism and heterogeneity of compute …

Task-based runtime systems have gained a lot of interest in recent years since they support
separating the specification of parallel computations from the concrete mapping onto a …

Performance tuning of scientific codes often requires tuning many different aspects like
vectorization, OpenMP synchronization, MPI communication, and load balancing. The …

Abstract (EN) The efficient parallel execution of scientific applications is a key challenge in
high-performance computing (HPC). With growing parallelism and heterogeneity of compute …

Ensuring software correctness is a challenging task to accomplish with the increasingly
large and complex functional as well as non-functional requirements of modern software …

Matrix computation is considered to be the core of many machine learning and graph
algorithm workloads. In traditional single-node age, numerical analysis platforms like R and …