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Blaschke, Johannes

Publications and source records attributed to Blaschke, Johannes.

JACC: Leveraging HPC Meta-Programming and Performance Portability with the Just-in-Time and LLVM-based Julia Language

We present JACC (Julia for Accelerators), the first high-level, and performance-portable model for the just-in-time and LLVM-based Julia language. JACC provides a unified and lightweight front end across different back ends available in Julia, enabling the same Julia code to run efficiently on many HPC CPU and GPU targets. We evaluated the performance of JACC for common HPC kernels as well as for the most computationally demanding kernels used in applications, HPCCG, a supercomputing benchmark test for sparse domains, and HARVEY, a blood flow simulator to assist in the diagnosis and treatment of patients suffering from vascular diseases. We carried out the performance analysis on the most advanced US DOE supercomputers: Aurora, Frontier, and Perlmutter. Overall, we show that JACC has a negligible overhead versus vendor-specific solutions, reporting GPU speedups with no extra cost to programmability.

Valero-Lara, Pedro↗

LBNL Superfacility Project Report

The Superfacility model is designed to leverage HPC for experimental science. It is more than simply a model of connected experiment, network, and HPC facilities; it encompasses the full ecosystem of infrastructure, software, tools, and expertise needed to make connected facilities easy to use. The three-year Lawrence Berkeley National Laboratory (LBNL) Superfacility project was initiated in 2019 to coordinate work being performed at LBNL to support this model, and to provide a coherent and comprehensive set of science requirements to drive existing and new work. A key component of the project was the in-depth engagements with eight science teams that represent challenging use cases across the DOE Office of Science.

97 MATHEMATICS AND COMPUTING↗

MTIP Single Particle Imaging (Spinifel) v0.1.0

Performs Multi-Tiered Iterative Phasing for Single Particle Imaging. The Spinifel code uses distributed computing (using MPI and Legion) to spread work across many compute nodes. On-node computation is accelerated using GPGPUs (using CuPy, and CUDA).

Blaschke, Johannes↗