Resources

This presentation introduces an innovative approach that combines Large Language Models (LLMs) and differentiable rendering techniques to automate the construction of digital twins. In our approach, we employ LLMs to guide and optimize the placement of objects in digital twin scenarios. This is achieved by integrating LLMs with differentiable rendering, a method traditionally used for optimizing object positions in computer graphics based on image pixel loss. Our technique enhances this process by incorporating a second modality, namely Lidar data, resulting in faster convergence and improved accuracy. This fusion of sensor inputs proves invaluable, especially for applications like autonomous vehicles, where establishing the precise location of multiple actors in a scene is crucial. Our methodology involves several key steps: (1) Generating a point cloud of the scene via ray casting, (2) Extracting lightweight geometry from the point cloud using PlaneSLAM, (3) Creating potential camera paths through the scene, (4) Selecting the most suitable camera path by leveraging the LLM in conjunction with image segmentation and classification, and (5) Rendering the camera flight path from its origin to the final destination. The technical backbone of this system includes the use of Mitsuba for ray tracing, powered by Intel's Embree ray tracing library. This setup encompasses Lidar simulation, image rendering, and a final differentiable rendering step for precise camera positioning. Future iterations may incorporate Intel OSPRay for enhanced Lidar-like ray casting and image rendering, with a possible integration of Mitsuba for differentiable render camera positioning. The machine learning inference chain utilizes a pre-trained LLM from OpenAI accessed via LangChain, coupled with GroundingDINO for zero-shot image segmentation and classification within PyTorch. This entire workflow is optimized for performance on the latest generation of Intel CPUs. This presentation will delve into the technical details of this approach, demonstrating its efficacy in automating digital twin construction and its potential applications in various industries, particularly in the realm of autonomous vehicle navigation and scene understanding.

Keyword(s): LLMs,differentiable rendering,digital twins,ray tracing,Lidar simulation,autonomous vehicles,in situ visualization

The Aurora exascale system is currently being deployed at Argonne National Lab. The system, utilizing Intel’s new Data Center Max Series GPUs (a.k.a. PVC) and Xeon Max Series CPU with HBM, will provide a uniquely powerful platform for leading-edge HPC, AI, and data-intensive computing applications. Scientists at Argonne National Laboratory, in collaboration with the Exascale Computing Project, Intel, and several other institutions, are preparing several dozen applications and workflows to run at scale on the Aurora system. This talk will present an overview of the Aurora system and highlights from the experience of preparing applications for the system. In addition, promising early performance results on the Aurora hardware will be shown.

Keyword(s): Exascale,Aurora,PVC,Ponte Vecchio,GPU Max,CPU Max,HBM,Data Center Max Series GPUs,Xeon Max Series CPU with HBM

Mapping Cores, CHAs, and Addresses in the Xeon Platinum 8380 by John McCalpin, Texas Advanced Computing Center, University of Texas at Austin

Keyword(s): Xeon Platinum 8380,Mapping Intel Xeon Processors,Ice Lake,conventions for numbering CHAs and cores

Improving MPI+Threads with MPIX_Stream by Ken Raffenetti, Argonne National Laboratory

Keyword(s): MPI+Threads,MPIX_Stream,MPI Asynchronous Progress,Virtual Communication Interface (VCI),Stream Communicator,MPI Progress Model

Keynote: New Era for Intel HPC Acceleration: Architecture, Systems & Software by Hong Jiang, Intel Corporation

Keyword(s): Exascale Compute Platform,oneAPI Software Stack,Ponte Vecchio,Application Performance,Zettascale

Keynote on the future of HPC and scientific discovery by Rick Stevens, Argonne National Laboratory

Keyword(s): Exascale,Science,Machine Learning,CPU,GPU,LLMs,Inverse Design,DeepFix

Recovery of Distributed Iterative Solvers for Linear Systems with Non-Volatile RAM by Yehonatan Fridman, Israel Atomic Energy Commission and Ben-Gurion University

Keyword(s): Exascale,Non-Volatile RAM (NVRAM),Persistent Memory,Linear Iterative Solvers,Exact State Reconstruction (ESR),Preconditioned Conjugate Gradient (PCG)

Best Practices for Benchmarking Diverse Architectures with Varied Workloads (agenda slide)

Keyword(s): IXPUG BoF at SC21 agenda slide

IXPUG Overview and Activities (IXPUG BoF at SC21)

Keyword(s): IXPUG overview and activities

A Simple Cost-model for Comparing Diverse Architectures

Keyword(s): cost model,diverse hardware,time-to-solution,memory footprint,result depreciation,compute cost,cost-per-run,Amdahl’s law