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In recent years, there has been a growing interest in developing high-performance implementations of drug discovery processing software. To target modern GPU architectures, such applications are mostly written in proprietary languages such as ...

• Enabling fast drug discovery deploying in urgent computing scenarios.
• Analyzing high-level SYCL 2020 semantics in real-world application scenario.
• Quantifying performance portability in heterogeneous systems.
• Evaluating SYCL ...

The advancement of traditional CPU architectures is seeing diminished returns due to the slowing of Moore’s Law and end of Dennard Scaling. FPGA-based accelerators provide a new path forward for High-Performance Computing. Instead of being confined to ...

In order to make the best use of the diverse hardware architectures in present and future high-performance computers, developers and maintainers of scientific simulation codes strive for performance portability. The goal is to reach a good fraction of ...

We present a massively parallel lockstep algorithm for dualizing large numbers of surface triangulation graphs, and an effective implementation for CPU, GPU and multi-GPU. The algorithm is fully combinatorial, i.e., it does not require or use a planar or ...

SYCL 2020 provides a host_task feature [1], whereby arbitrary C++ functions can be invoked as a node within the SYCL Directed Acyclic Graph (DAG). This feature is useful for interleaving host code between SYCL kernels and other commands, as well as for ...

The open SYCL standard has established itself as a cross-vendor, cross-platform means to develop software which benefits from GPU and accelerator parallelism. Inherent difficulties in portability between and debuggability of programs for these targets ...

In response to the growing demand to incorporate LLVM sanitizers into heterogeneous computing environments, this poster presents the development of a Unified Sanitizer Framework for SYCL programming.

The challenge lies in designing a unified framework ...

To achieve high performance, modern HPC systems take advantage of heterogeneous GPU architectures. Often these GPUs are programmed using a vendor preferred parallel programming model. Unfortunately, this often results in application code that is not ...

SYCL provides programmers with four, and in the case of AdaptiveCpp even five, ways for calling and writing a device kernel. This paper analyzes the performance of these diverse kernel invocation types for DPC++ and AdaptiveCpp as SYCL implementations ...

Lidar plays an increasingly important role in the world [7]. By providing distance information about its surrounding environment to the perception system. For example, in the autonomous vehicle, Lidar can significantly enhance and correct the actions ...

High-Performance Computing is the target system for virtual screening applications, which aim to suggest which candidates to test in the drug discovery process. The HPC heterogeneity of modern systems raises the functional and performance portability ...

Since widening its scope and becoming its independent working group 5 years ago, SYCL has evolved into the alternative to CUDA by championing an open-source and standards-based approach to accelerator offload, including GPUs. SYCL became the cornerstone ...

Recently, machine learning has established itself as a valuable tool for researchers to analyze their data and draw conclusions in various scientific fields, such as High Energy Physics (HEP). Commonly used machine learning libraries, such as Keras and ...

The world’s largest particle accelerator, located at CERN, produces petabytes of data that need to be analysed efficiently, to study the fundamental structures of our universe. ROOT RDataFrame is an open-source C++ data analysis interface, developed for ...

Today, the SYCL standard represents the most advanced programming model for heterogeneous computing, delivering both productivity, portability, and performance in pure C++17. SYCL 2020, in particular, represents a major enhancement that pushes the ...

Expressing data parallel programs using C++ standard parallelism is attractive not only due to the simplicity of the model, but also due to its highly idiomatic nature. This programming model, commonly referred to as stdpar, can also be used for ...

Edge computing is essential to handle increasing data volumes and processing capacities. It provides real-time and secure data processing near data sources, like smart devices, alleviating cloud computing energy use, and saving network bandwidth. ...$\mathrm{}$

Similar to other programming models, compilers for SYCL, the open programming model for heterogeneous computing based on C++, would benefit from access to higher-level intermediate representations. The loss of high-level structure and semantics caused by ...

Bioinformatics and computational biology are two fields that have been exploiting GPUs for more than two decades, with being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong ...