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CUDA is designed specifically for NVIDIA GPUs and is not compatible with non-NVIDIA devices. Enabling CUDA execution on alternative backends could greatly benefit the hardware community by fostering a more diverse software ecosystem.

To address the need for portability, our objective is to develop a framework that meets key requirements, such as extensive coverage, comprehensive end-to-end support, superior performance, and hardware scalability. Existing solutions that translate CUDA source code into other high-level languages, however, fall short of these goals.

In contrast to these source-to-source approaches, we present a novel framework, CuPBoP , which treats CUDA as a portable language in its own right. Compared to two commercial source-to-source solutions, CuPBoP offers a broader coverage and superior performance for the CUDA-to-CPU migration. Additionally, we evaluate the performance of CuPBoP against manually optimized CPU programs, highlighting the differences between CPU programs derived from CUDA and those that are manually optimized.

Furthermore, we demonstrate the hardware scalability of CuPBoP by showcasing its successful migration of CUDA to AMD GPUs.

To promote further research in this field, we have released CuPBoP as an open-source resource.

 

Autonomous drones (UAVs) have rapidly grown in popularity due to their form factor, agility, and ability to operate in harsh or hostile environments. Drone systems come in various form factors and configurations and operate under tight physical parameters. Further, it has been a significant challenge for architects and researchers to develop optimal drone designs as open-source simulation frameworks either lack the necessary capabilities to simulate a full drone flight stack or they are extremely tedious to setup with little or no maintenance or support. In this paper, we develop and present UniUAVSim, our fully open-source co-simulation framework capable of running software-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulations concurrently. The paper also provides insights into the abstraction of a drone flight stack and details how these abstractions aid in creating a simulation framework which can accurately provide an optimal drone design given physical parameters and constraints. The framework was validated with real-world hardware and is available to the research community to aid in future architecture research for autonomous systems. 

Centered on modern C++ and the SYCL standard for heterogeneous programming, Data Parallel C++ (dpc++) and Intel's oneAPI software ecosystem aim to lower the barrier to entry for the use of accelerators like FPGAs in diverse applications. In this work, we consider the usage of FPGAs for scientific computing, in particular with a multigrid solver, MueLu. We report on early experiences implementing kernels of the solver in DPC++ for execution on Stratix 10 FPGAs, and we evaluate several algorithmic design and implementation choices. These choices not only impact performance, but also shed light on the capabilities and limitations of DPC++ and oneAPI. 

Arm HPC has succeeded in scaling up in the supercomputing space with the deployment of systems like RIKEN’s Fugaku supercomputer and Sandia’s Astra cluster. At the same time, the onboarding of new users to the Arm HPC ecosystem has never been more complex due to an overabundance of compilers, libraries, and build options for tools and applications. This work investigates one particular method to ease the integration of new users into the space of Arm HPC through the use of Open OnDemand to provide a consistent and easy-to-use front-end for Georgia Tech’s A64FX cluster, Octavius. We detail the motivations for this deployment as well as the potential pitfalls in integrating with an Arm A64FX environment. User-motived applications that incorporate the interactive usage of virtual desktops and Jupyter notebooks are discussed as motivating user workflows, and we provide some context on how future deployments might look with combined Arm and Open OnDemand integration.