Related papers: Enabling Kernel Bypass Networking on gem5

Across applications, DRAM is a significant contributor to the overall system power, with the DRAM access energy per bit up to three orders of magnitude higher compared to on-chip memory accesses. To improve the power efficiency, DRAM…

There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimick biology. They use neural networks which can be trained to…

Context plays a crucial role in visual recognition as it provides complementary clues for different learning tasks including image classification and annotation. As the performances of these tasks are currently reaching a plateau, any extra…

Blockchain, like any other complex technology, needs a strong testing methodology to support its evolution in both research and development contexts. Setting up meaningful tests for permissionless blockchain technology is a notoriously…

The increasing prevalence of cloud-native technologies, particularly containers, has led to the widespread adoption of containerized deployments in data centers. The advancement of deep neural network models has increased the demand for…

Structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or…

The growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC). Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of…

Large-scale tensor network simulations are crucial for developing robust complexity-theoretic bounds on classical quantum simulation, enabling circuit cutting approaches, and optimizing circuit compilation, all of which aid efficient…

Growing deployment of power and energy efficient throughput accelerators (GPU) in data centers demands enhancement of power-performance co-optimization capabilities of GPUs. Realization of exascale computing using accelerators requires…

Adding new hardware features to a cloud computing server requires testing both the functionalities and the performance of the new hardware mechanisms. However, commonly used cloud computing server workloads are not well-represented by the…

High throughput is of particular interest in data center and HPC networks. Although myriad network topologies have been proposed, a broad head-to-head comparison across topologies and across traffic patterns is absent, and the right way to…

Data center networking is the central infrastructure of the modern information society. However, benchmarking them is very challenging as the real-world network traffic is difficult to model, and Internet service giants treat the network…

Memory disaggregation has emerged as an alternative to traditional server architecture in data centers. This paper introduces DRackSim, a simulation infrastructure to model rack-scale hardware disaggregated memory. DRackSim models multiple…

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel…

Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and sparsity of the data. The local and global structure of the…

The rapidly growing popularity and scale of data-parallel workloads demand a corresponding increase in raw computational power of GPUs (Graphics Processing Units). As single-GPU systems struggle to satisfy the performance demands, multi-GPU…

Protected user-level libraries have been proposed as a way to allow mutually distrusting applications to safely share kernel-bypass services. In this paper, we identify and solve several previously unaddressed obstacles to realizing this…

Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues to building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware…

In order to bring quantum networks into the real world, we would like to determine the requirements of quantum network protocols including the underlying quantum hardware. Because detailed architecture proposals are generally too complex…

Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While standalone accelerators tailored for specific application…