This paper proposes a fully autonomous execution model for multi-GPU applications that
completely excludes the involvement of the CPU beyond the initial kernel launch. In a typical …

In recent years, GPUs have become the preferred accelerators for HPC and ML applications
due to their parallelism and fast memory bandwidth. While GPUs boost computation, inter …

We present GPUrdma, a GPU-side library for performing Remote Direct Memory Accesses
(RDMA) across the network directly from GPU kernels. The library executes no code on …

We propose a new system design for connecting hardware and FPGA accelerators to the
network, allowing the accelerator to directly control commodity Network Interface Cards …

HPC architects are currently facing myriad challenges from ever tighter power constraints
and changing workload characteristics. In this article, we discuss the current state of FPGAs …

Increasing number of MPI applications are being ported to take advantage of the compute
power offered by GPUs. Data movement on GPU clusters continues to be the major …

Modern heterogeneous supercomputing systems are comprised of compute blades that offer
CPUs and GPUs. On such systems, it is essential to move data efficiently between these …

We investigate the dynamics of monitored memory resource utilizations in an operating
system under stress using quantitative methods of fractal analysis. In the experiments, we …

Wavelength routed optical switching promises low power and latency networking for data
centres, but requires a wideband wavelength tuneable source (WTS) capable of sub …

Over the last decade, CUDA and the underlying GPU hardware architecture have
continuously gained popularity in various high-performance computing application domains …