In computational fluid dynamics, the Lattice Boltzmann method is a computational technique that has gained popularity due to its flexibility in handling complex geometries and turbulence models, and its unique suitability for massive parallel processing. The method, which discretizes both space and velocity into a lattice structure, has been the subject of highly sophisticated implementation by academia and industry, resulting in very large and engineered code bases. This article introduces miniLB , to the best of our knowledge the first SYCL-based mini-application for the Lattice Boltzmann method. Thanks to its minimalist structure, miniLB is a perfect benchmark to address four key aspects of lattice Boltzmann implementations: (a) GPU acceleration, thanks to an efficient implementation in SYCL capable of abstracting complex fluid dynamics simulations across heterogeneous computing systems; (b) performance portability, with an efficient mapping to SYCL semantics focused on performance portability, evaluated on GPUs from different vendors; (c) mixed precision, with four different variations exploiting combinations of double, single, and half floating-point representations; (d) flexibility, demonstrated through four different use cases, including Lid-driven cavity, Von Karmann street, Poiseuille flow, and Taylor–Green vortex. The results of miniLB , compared to a manually tuned FORTRAN version, demonstrate the effectiveness of miniLB in assessing performance portability across different hardware, while providing valuable insights for optimizing large-scale lattice Boltzmann simulations in modern massively parallel computing systems.

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 CUDA or HIP. However, with the increasing heterogeneity of modern HPC systems and the availability of accelerators from multiple hardware vendors, it has become critical to be able to efficiently execute drug discovery pipelines on multiple large-scale computing systems, with the ultimate goal of working on urgent computing scenarios. This article presents the challenges of migrating LiGen, an industrial drug discovery software pipeline, from CUDA to the SYCL programming model, an industry standard based on C++ that enables heterogeneous computing. We perform a structured analysis of the performance portability of the SYCL LiGen platform, focusing on different aspects of the approach from different perspectives. First, we analyze the performance portability provided by the high-level semantics of SYCL, including the most recent group algorithms and subgroups of SYCL 2020. Second, we analyze how low-level aspects such as kernel occupancy and register pressure affect the performance portability of the overall application. The experimental evaluation is performed on two different versions of LiGen, implementing two different parallelization patterns, by comparing them with a manually optimized CUDA version, and by evaluating performance portability using both known and ad hoc metrics. The results show that, thanks to the combination of high-level SYCL semantics and some manual tuning, LiGen achieves native-comparable performance on NVIDIA, while also running on AMD GPUs.

Abstract Virtual screening is an early stage in the drug discovery process that selects the most promising candidates. In the urgent computing scenario, finding a solution in the shortest time frame is critical. Any improvement in the performance of a virtual screening application translates into an increase in the number of candidates evaluated, thereby raising the probability of finding a drug. In this paper, we show how we can improve application throughput using Out-of-kernel optimizations. They use input features, kernel requirements, and architectural features to rearrange the kernel inputs, executing them out of order, to improve the computation efficiency. These optimizations’ implementations are designed on an extreme-scale virtual screening application, named LiGen, that can hinge on CUDA and SYCL kernels to carry out the computation on modern supercomputer nodes. Even if they are tailored to a single application, they might also be of interest for applications that share a similar design pattern. The experimental results show how these optimizations can increase kernel performance by 2 $$\times$$ × , respectively, up to 2.2 $$\times$$ × in CUDA and up to 1.9 $$\times$$ × , in SYCL. Moreover, the reported speedup can be achieved with the best-proposed parameterization, as shown by the data we collected and reported in this manuscript.

Message Passing Interface (MPI) is a well-known standard for programming distributed and HPC systems. While the community has been continuously improving MPI to address the requirements of next-generation architectures and applications, its interface has not substantially evolved. In fact, MPI only provides an interface to C and Fortran and does not support recent features of modern C++. Moreover, MPI programs are error-prone and subject to different syntactic and semantic errors. This paper introduces EMPI, an Enhanced Message Passing Interface based on modern C++, which is directly mapped to the OpenMPI implementation and exploits modern C++ for safe and efficient distributed programming. EMPI proposes novel C++RAII-based semantics and constant specialization to prevent error-prone code patterns such as parameter mismatch, and reduce the overhead of handling multiple objects and perinvocation time. Consequently, EMPI programs are safer: six out of nine well-known MPI error patterns do not occur while correctly using EMPI semantics. Experimental results on five microbenchmarks and two applications on a large-scale cluster using up to 1024 processes show that EMPI's performance is very similar to native MPI and considerably faster than the MPL C++ interface.

Today digital revolution is having a dramatic impact on the pharmaceutical industry and the entire healthcare system. The implementation of machine learning, extreme-scale computer simulations, and big data analytics in the drug design and development process offers an excellent opportunity to lower the risk of investment and reduce the time to the patient.