4 citations · 16 across the 14 of their papers we have counts for
15 papers
Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
Zhengyong Huang, Ning Jiang, Xingwen Sun +4
Image segmentation is pivotal in medical image analysis, facilitating clinical diagnosis, treatment planning, and disease evaluation. Deep learning has significantly advanced autom…
A Unifying Framework to Enable Artificial Intelligence in High Performance Computing Workflows
Jens Domke, Mohamed Wahib, Anshu Dubey +2
Current trends point to a future where large-scale scientific applications are tightly-coupled HPC/AI hybrids. Hence, we urgently need to invest in creating a seamless, scalable fr…
A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network
Nils Blach, Maciej Besta, Daniele De Sensi +10
Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption…
Myths and Legends in High-Performance Computing
Satoshi Matsuoka, Jens Domke, Mohamed Wahib +2
In this thought-provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We gathered these myths from co…
High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs
William S. Moses, Ivan R. Ivanov, Jens Domke +3
While parallelism remains the main source of performance, architectural implementations and programming models change with each new hardware generation, often leading to costly app…
Preparing for the Future -- Rethinking Proxy Apps
Satoshi Matsuoka, Jens Domke, Mohamed Wahib +5
A considerable amount of research and engineering went into designing proxy applications, which represent common high-performance computing workloads, to co-design and evaluate the…
At the Locus of Performance: Quantifying the Effects of Copious 3D-Stacked Cache on HPC Workloads
Jens Domke, Emil Vatai, Balazs Gerofi +9
Over the last three decades, innovations in the memory subsystem were primarily targeted at overcoming the data movement bottleneck. In this paper, we focus on a specific market tr…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
A64FX -- Your Compiler You Must Decide!
Jens Domke
The current number one of the TOP500 list, Supercomputer Fugaku, has demonstrated that CPU-only HPC systems aren't dead and CPUs can be used for more than just being the host contr…
EvalNet: A Practical Toolchain for Generation and Analysis of Extreme-Scale Interconnects
Maciej Besta, Patrick Iff, Marcel Schneider +10
The diversity of communication paths in a network, especially non-minimal paths, is a key enabler of performance at extreme scales. We present EvalNet, a toolchain for scalable gen…
Matrix Engines for High Performance Computing:A Paragon of Performance or Grasping at Straws?
Jens Domke, Emil Vatai, Aleksandr Drozd +8
Matrix engines or units, in different forms and affinities, are becoming a reality in modern processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep L…
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen +5
The dedicated memory of hardware accelerators can be insufficient to store all weights and/or intermediate states of large deep learning models. Although model parallelism is a via…
High-Performance Routing with Multipathing and Path Diversity in Ethernet and HPC Networks
Maciej Besta, Jens Domke, Marcel Schneider +5
The recent line of research into topology design focuses on lowering network diameter. Many low-diameter topologies such as Slim Fly or Jellyfish that substantially reduce cost, po…
White Paper from Workshop on Large-scale Parallel Numerical Computing Technology (LSPANC 2020): HPC and Computer Arithmetic toward Minimal-Precision Computing
Roman Iakymchuk, Daichi Mukunoki, Artur Podobas +15
In numerical computations, precision of floating-point computations is a key factor to determine the performance (speed and energy-efficiency) as well as the reliability (accuracy…
Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?
Jens Domke, Kazuaki Matsumura, Mohamed Wahib +6
Among the (uncontended) common wisdom in High-Performance Computing (HPC) is the applications' need for large amount of double-precision support in hardware. Hardware manufacturers…