相关论文: Fast Parallel I/O on Cluster Computers
Lossy compression is one of the most efficient solutions to reduce storage overhead and improve I/O performance for HPC applications. However, existing parallel I/O libraries cannot fully utilize lossy compression to accelerate parallel…
Efficient virtualization of CPU and memory is standardized and mature. Capabilities such as Intel VT-x [3] have been added by manufacturers for efficient hypervisor support. In contrast, virtualization of a block device and its presentation…
As the computing power of large-scale HPC clusters approaches the Exascale, the gap between compute capabilities and storage systems is ever widening. In particular, the popular High Performance Computing (HPC) application, the Weather…
This paper presents an inverted-file k-means clustering algorithm (IVF) suitable for a large-scale sparse data set with potentially numerous classes. Given such a data set, IVF efficiently works at high-speed and with low memory…
When processing large amounts of data, the rate at which reading and writing can take place is a critical factor. High energy physics data processing relying on ROOT is no exception. The recent parallelisation of LHC experiments' software…
With the approach of Exascale computing power for large-scale High Performance Computing (HPC) clusters, the gap between compute capabilities and storage systems is growing larger. This is particularly problematic for the Weather Research…
Sheer amount of petabyte scale data foreseen in the LHC experiments require a careful consideration of the persistency design and the system design in the world-wide distributed computing. Event parallelism of the HENP data analysis enables…
This paper designs and implements a high availability clusters and incorporated with load balance infrastructure of web servers. The paper described system can provide full facilities to the website hosting provider and large business…
Modern supercomputers are increasingly requiring the presence of accelerators and co-processors. However, it has not been easy to achieve good performance on such heterogeneous clusters. The key challenge has been to ensure good load…
High Performance Compute (HPC) clusters often produce intermediate files as part of code execution and message passing is not always possible to supply data to these cluster jobs. In these cases, I/O goes back to central distributed storage…
Synchronization is likely the most critical performance killer in shared-memory parallel programs. With the rise of multi-core and many-core processors, the relative impact on performance and energy overhead of synchronization is bound to…
Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and…
We introduce a new approach to enable an open and public parallel machine which is accessible for multi users with multi jobs belong to different blocks running at the same time. The concept is required especially for parallel machines…
Parallel I/O refers to the ability of scientific programs to concurrently read/write from/to a single file from multiple processes executing on distributed memory platforms like compute clusters. In the HPC world, I/O becomes a significant…
The widespread diffusion of compute-intensive edge-AI workloads and the stringent demands of modern autonomous systems require advanced heterogeneous embedded architectures. Such architectures must support high-performance and reliable…
The High Level Trigger (HLT) of the future ALICE heavy-ion experiment has to reduce its input data rate of up to 25 GB/s to at most 1.25 GB/s for output before the data is written to permanent storage. To cope with these data rates a large…
Regional hydrology studies are often supported by high resolution simulations of subsurface flow that require expensive and extensive computations. Efficient usage of the latest high performance parallel computing systems becomes a…
IT based scientific research requires high computational resources. The limitation on funding and infrastructure led the high performance computing era from supercomputer to cluster and grid computing technology. Parallel application…
More and more massive parallel codes running on several hundreds of thousands of cores enter the computational science and engineering domain, allowing high-fidelity computations on up to trillions of unknowns for very detailed analyses of…
Modern high-performance computing architectures (Multicore, GPU, Manycore) are based on tightly-coupled clusters of processing elements, physically implemented as rectangular tiles. Their size and aspect ratio strongly impact the achievable…