A GPU-Accelerated In-Memory Metadata Management Scheme forLarge-Scale Parallel File Systems

Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing nodes.Applications running on such a large-scale computing system are likely to spawn millions of parallel processes,which usually generate a burst of I/O requests,introducing a great challenge into the metadata management of underlying parallel file systems.The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner,which will inevitably confront with serious network and consistence problems.This work instead pursues to enhance the metadata performance in the scale-up manner.Specifically,we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel.Our proposal designs a novel metadata server architecture,which employs CPU to interact with file system clients,while offloading the computing tasks about metadata into GPU.To take full advantages of the parallelism existing in GPU,we redesign the in-memory data structure for the name space of file systems.The new data structure can perfectly fit to the memory architecture of GPU,and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently.We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal,and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50%under typical metadata operations.The superiority is strengthened further on high concurrent scenarios,e.g.,the high-performance computing systems supporting millions of parallel threads.

Advancement in CFD and Responsive AI to Examine Cardiovascular Pulsatile Flow in Arteries:A Review

This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics(CFD)in biomedical applications.Beyond its various engineering applications,CFD has started to establish a presence in the biomedical field.Cardiac abnormality,a familiar health issue,is an essential point of investigation by research analysts.Diagnostic modalities provide cardiovascular structural information but give insufficient information about the hemodynamics of blood.The study of hemodynamic parameters can be a potential measure for determining cardiovascular abnormalities.Numerous studies have explored the rheological behavior of blood experimentally and numerically.This paper provides insight into how researchers have incorporated the pulsatile nature of the blood experimentally,numerically,or through various simulations over the years.It focuses on how machine learning platforms derive outputs based on mass and momentum conservation to predict the velocity and pressure profile,analyzing various cardiac diseases for clinical applications.This will pave the way toward responsive AI in cardiac healthcare,improving productivity and quality in the healthcare industry.The paper shows how CFD is a vital tool for efficiently studying the flow in arteries.The review indicates this biomedical simulation and its applications in healthcare using machine learning and AI.Developing AI-based CFD models can impact society and foster the advancement towards responsive AI.

Fast compression of computer-generated holographic images based on a GPU-accelerated skip-dimension vector quantization method

A method for fast and low bit-rate compression of digital holograms based on a new vector quantization （VQ） method known as the skip-dimension VQ （SDVQ） is proposed. Briefly, a complex hologram is converted into a real off-axis hologram, and partitioned into a set of image vectors. The image vectors are passed into a graphic processing unit （GPU）, and compressed through SDVQ into a set of code indices considerably smaller in data size than the source hologram. Experimental evaluation reveals that our scheme is capable of compressing a digital hologram to a compression ratio of over 500 times, in approximately 20-22 ms.