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This paper presents HyperGraph ROS, an open-source robot operating system that unifies intra-process, inter-process, and cross-device computation into a computational hypergraph for efficient message passing and parallel execution. In order…

We introduce a mapping framework for deep learning inference that takes advantage of predictable neural network behavior to plan both computation and communication ahead of time. The framework generates a unified stream of instructions and…

The simplex algorithm has been successfully used for many years in solving linear programming (LP) problems. Due to the intensive computations required (especially for the solution of large LP problems), parallel approaches have also…

With the rapid evolution of GPU architectures, the heterogeneity of model training infrastructures is steadily increasing. In such environments, effectively utilizing all available heterogeneous accelerators becomes critical for distributed…

We present and evaluate the ExaNeSt Prototype, a liquid-cooled rack prototype consisting of 256 Xilinx ZU9EG MPSoCs, 4 TBytes of DRAM, 16 TBytes of SSD, and configurable interconnection 10-Gbps hardware. We developed this testbed in…

The attention layer, a core component of Transformer-based LLMs, brings out inefficiencies in current GPU systems due to its low operational intensity and the substantial memory requirements of KV caches. We propose a High-bandwidth…

High performance computing (HPC) devices is no longer exclusive for academic, R&D, or military purposes. The use of HPC device such as supercomputer now growing rapidly as some new area arise such as big data, and computer simulation. It…

Ising machines are specialized computers for finding the lowest energy states of Ising spin models, onto which many practical combinatorial optimization problems can be mapped. Simulated bifurcation (SB) is a quantum-inspired parallelizable…

Removing the CPU from the communication fast path is essential to efficient GPU-based ML and HPC application performance. However, existing GPU communication APIs either continue to rely on the CPU for communication or rely on APIs that…

The Bulk Synchronous Parallel(BSP) computational model has emerged as the dominant distributed framework to build large-scale iterative graph processing systems. While its implementations(e.g., Pregel, Giraph, and Hama) achieve high…

Database algorithms play a crucial part in systems biology studies by identifying proteins from mass spectrometry data. Many of these database search algorithms incur huge computational costs by computing similarity scores for each pair of…

Recent research on vision backbone architectures has predominantly focused on optimizing efficiency for hardware platforms with high parallel processing capabilities. This category increasingly includes embedded systems such as mobile…

Many FPGAs vendors have recently included embedded processors in their devices, like Xilinx with ARM-Cortex A cores, together with programmable logic cells. These devices are known as Programmable System on Chip (PSoC). Their ARM cores…

In this paper, we introduce a software-defined framework that enables the parallel utilization of all the programmable processing resources available in heterogeneous system-on-chip (SoC) including FPGA-based hardware accelerators and…

This work gives an overview of the PCI-Express board $\pi$LUP, focusing on the motivation that led to its development, the technological choices adopted and its performance. The $\pi$LUP card was designed by INFN and University of Bologna…

One of the barriers to the adoption of parallel computing is the inherent complexity of its programming. The Open Multi-Processing (OpenMP) Application Programming Interface (API) facilitates such implementations, providing high abstraction…

We developed a PCI interface for GRAPE systems. GRAPE(GRAvity piPE) is a special-purpose computer for gravitational N-body simulations. A GRAPE system consists of GRAPE processor boards and a host computer. GRAPE processors perform the…

Data-intensive scientific and commercial applications increasingly require frequent movement of large datasets from one site to the other(s). Despite growing network capacities, these data movements rarely achieve the promised data transfer…

Homomorphic Encryption (HE) provides strong data privacy for cloud services but at the cost of prohibitive computational overhead. While GPUs have emerged as a practical platform for accelerating HE, there remains an order-of-magnitude…

The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4~TB/s in real time. Because LHCb's physics objectives require that the full detector information for every LHC bunch crossing is read…