System designers typically use well-studied benchmarks to evaluate and improve new architectures and compilers. We design tomorrow's systems based on yesterday's applications. In this paper we investigate an emerging application, 3D scene understanding, likely to be signi cant in the mobile space in the near future. Until now, this application could only run in real-time on desktop GPUs. In this work, we examine how it can be mapped to power constrained embedded systems. Key to our approach is the idea of incremental co-design exploration, where optimization choices that concern the domain layer are incrementally explored together with low-level compiler and architecture choices. The goal of this exploration is to reduce execution time while minimizing power and meeting our quality of result objective. As the design space is too large to exhaustively evaluate, we use active learning based on a random forest predictor to nd good designs. We show that our approach can, for the rst time, achieve dense 3D mapping and tracking in the real-time range within a 1W power budget on a popular embedded device. This is a 4.8x execution time improvement and a 2.8x power reduction compared to the state-of-the-art
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Our work seeks to transform how new and emergent variants of pandemic causing viruses, specially SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences, and then finetuning a SARS-CoV-2 specific model on 1.5 million genomes, we show that GenSLM can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLM represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate the scaling of GenSLMs on both GPU-based supercomputers and AI-hardware accelerators, achieving over 1.54 zettaflops in training runs. We present initial scientific insights gleaned from examining GenSLMs in tracking the evolutionary dynamics of SARS-CoV-2, noting that its full potential on large biological data is yet to be realized.
Scientists from many different fields have been developing Bulk-Synchronous MPI applications to simulate and study a wide variety of scientific phenomena. Since failure rates are expected to increase in larger scale future HPC systems, providing efficient fault-tolerance mechanisms for this class of applications is paramount.The global-restart model has been proposed to decrease the time of failure recovery in Bulk-Synchronous applications by allowing a fast reinitialization of MPI. However, the current implementations of this model have several drawbacks: they lack efficiency; their scalability have not been shown; they require the use of the MPI profiling interface, which precludes the use of tools. In this paper, we present ERE-INIT, an implementation of the global-restart model that addresses these problems.Our key idea and optimization is the co-design of basic fault-tolerance mechanisms, such as failure detection, notification, and recovery, between MPI and the resource manager, in contrast to current approaches on which these mechanisms are implemented in MPI only. We demonstrate EREINIT in three HPC programs and show that it is up to four times more efficient than existing solutions at 4,096 processes.
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