Exascale computing and big data Page 1 56 COMMUNICATIONS OF THE ACM | JULY
2015 | VOL. 58 | NO. 7 contributed articles ILL US TRA TION B Y PETER BOLLINGER DOI:10.1145/2699414 …

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-
world applications in medicine, materials science, computer engineering, and data …

As high-end computing machines continue to grow in size, issues such as fault tolerance
and reliability limit application scalability. Current techniques to ensure progress across …

Energy consumption is one of the top challenges for achieving the next generation of
supercomputing. Codesign of hardware and software is critical for improving energy …

As the size of turbulent flow simulations continues to grow, in situ data compression is
becoming increasingly important for visualization, analysis, and restart checkpointing. For …

Application resilience is a key challenge that must be addressed in order to realize the
exascale vision. Process/node failures, an important class of failures, are typically handled …

This work complements our previous efforts in generating realistic fingerprint images for test
purposes. The main variability which characterizes the acquisition of a fingerprint through an …

High performance computing (HPC) systems use checkpoint-restart to tolerate failures.
Typically, applications store their states in checkpoints on a parallel file system (PFS). As …

We present DASH, a C++ template library that offers distributed data structures and parallel
algorithms and implements a compiler-free PGAS (partitioned global address space) …

This paper presents Bolt, a compiler-directed soft error recovery scheme, that provides fine-
grained and guaranteed recovery without excessive performance and hardware overhead …