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We describe a multi-graphics processing unit (GPU) implementation of the Ludwig application, which specialises in simulating a variety of complex fluids via lattice Boltzmann fluid dynamics coupled to additional physics describing complex fluid constituents. We describe our methodology in augmenting the original central processing unit (CPU) version with GPU functionality in a maintainable fashion. We present several optimisations that maximise performance on the GPU architecture through tuning for the GPU memory hierarchy. We describe how we implement particles within the fluid in such a way to avoid a major diversion of the CPU and GPU codebases, whilst minimising data transfer at each time step. We detail our halo-exchange communication phase for the code, which exploits overlapping to allow efficient parallel scaling to many GPUs. We present results showing that the application demonstrates excellent scaling to at least 8192 GPUs in parallel, the largest system tested at the time of writing. The GPU version (on NVIDIA K20X GPUs) is around 3.5–5 times faster that the CPU version (on fully utilised AMD Opteron 6274 16-core CPUs), comparing equal numbers of CPUs and GPUs. [ABSTRACT FROM AUTHOR]

We present our approach to making direct numerical simulations of turbulence with applications in sustainable shipping. We use modern Fortran and the spectral element method to leverage and scale on supercomputers powered by the Nvidia A100 and the recent AMD Instinct MI250X GPUs, while still providing support for user software developed in Fortran. We demonstrate the efficiency of our approach by performing the world's first direct numerical simulation of the flow around a Flettner rotor at Re = 30,000 and its interaction with a turbulent boundary layer. We present a performance comparison between the AMD Instinct MI250X and Nvidia A100 GPUs for scalable computational fluid dynamics. Our results show that one MI250X offers performance on par with two A100 GPUs and has a similar power efficiency based on readings from on-chip energy sensors. [ABSTRACT FROM AUTHOR]

Summary: Venetoclax is a BCL2 inhibitor with activity in relapsed/refractory (R/R) chronic lymphocytic leukaemia (CLL). We conducted a multi‐centre retrospective analysis of 105 R/R CLL patients who received venetoclax pre‐National Health Service commissioning. The median age was 67 years and median prior lines was 3 (range: 1–15). 48% had TP53 disruption. At ≥2 lines, 60% received a Bruton Tyrosine Kinase inhibitor (BTKi) and no prior phosphoinositide 3‐kinase inhibitor (Pi3Ki), 25% received a Pi3Ki and no prior BTKi, and 10% received both. Patients discontinued B cell receptor inhibitor (BCRi) because of toxicity in 44% and progression in 54%. Tumour lysis syndrome risk was low, intermediate or high in 27%, 25%, and 48% respectively. Overall response was 88% (30% complete response [CR]). The overall response rate was 85% (CR 23%) in BTKi‐exposed patients, 92% (CR 38%) in Pi3Ki‐exposed patients and 80% (CR 20%) in both (P = 0·59). With a median follow‐up of 15·6 months, 1‐year progression‐free survival was 65·0% and 1‐year overall survival was 75·1%. Dose reduction or temporary interruption did not result in an inferior progression‐free or discontinuation‐free survival. Risk of progression or death after stopping a prior BCRi for progression was double compared to those stopping for other reasons (predominantly toxicity) (Hazard Ratio 2·01 P = 0·05). Venetoclax is active and well tolerated in R/R CLL post ≥1 BCRi. Reason(s) for stopping BCRi influences venetoclax outcomes. [ABSTRACT FROM AUTHOR]

We present a case study of porting NekBone, a skeleton version of the Nek5000 code, to a parallel GPU-accelerated system. Nek5000 is a computational fluid dynamics code based on the spectral element method used for the simulation of incompressible flow. The original NekBone Fortran source code has been used as the base and enhanced by OpenACC directives. The profiling of NekBone provided an assessment of the suitability of the code for GPU systems, and indicated possible kernel optimizations. To port NekBone to GPU systems required little effort and a small number of additional lines of code (approximately one OpenACC directive per 1000 lines of code). The naïve implementation using OpenACC leads to little performance improvement: on a single node, from 16 Gflops obtained with the version without OpenACC, we reached 20 Gflops with the naïve OpenACC implementation. An optimized NekBone version leads to a 43 Gflop performance on a single node. In addition, we ported and optimized NekBone to parallel GPU systems, reaching a parallel efficiency of 79.9% on 1024 GPUs of the Titan XK7 supercomputer at the Oak Ridge National Laboratory. [ABSTRACT FROM AUTHOR]