Quantum computers must operate in concert with classical computers to deliver on the promise of quantum advantage for practical problems. To achieve that, it is important to understand how quantum and classical computing can interact together, and how one can characterize the scalability and efficiency of hybrid quantum–classical workflows. So far, early experiments with quantum-centric supercomputing workflows have been limited in scale and complexity. Here, we use a Heron quantum processor deployed on premises with the entire supercomputer Fugaku to perform the largest computation of electronic structure involving quantum and classical high-performance computing. We design a closed-loop workflow between the quantum processors and 152,064 classical nodes of Fugaku, to approximate the electronic structure of chemistry models beyond the reach of exact diagonalization, with accuracy comparable to some all-classical approximation methods. Our work pushes the limits of the integration of quantum and classical high-performance computing, showcasing computational resource orchestration at the largest scale possible for current classical supercomputers.

Vela is a cloud-native system designed for LLM training workloads built using off-the-shelf hardware, Linux KVM-based virtualization, and a virtualized RDMA over Converged Ethernet (RoCE) network. Vela virtual machines (VMs) support peer-to-peer DMA between the GPUs and SRIOV-based network interface. In this paper, we share Vela's key architectural aspects with details from an NVIDIA A100 GPU-based deployment in one of the IBM Cloud data centers. Throughout the paper, we share insights and experiences from designing, building, and operating the system over a ~2.5 year timeframe to highlight the capabilities of readily available software and hardware technologies and the improvement opportunities for future AI systems, thereby making AI infrastructure more accessible to a broader community. As we evaluated the system for performance at ~1500 GPU scale, we achieved ~80% of the ideal throughput while training a 50 billion parameter decoder model using model parallelism, and ~70% per GPU FLOPS compared to a single VM with the High-Performance Linpack benchmark.

HPC (high performance computing) applications come with a variety of requirements for computation, communication, and storage; and many of these requirements can be met with commodity technology available in public clouds. In this article, we report on results for several well-known HPC applications on IBM public cloud, and we describe best practices for running such applications on cloud systems in general. Our results show that public clouds are not only ready for HPC workloads, but they can provide performance comparable to, and in some cases better than, current supercomputers.

This paper explores the performance and optimization of the IBM Blue Gene/Q (BG/Q) five dimensional torus network on up to 16K nodes. The BG/Q hardware supports multiple dynamic routing algorithms and different traffic patterns may require different algorithms to achieve best performance. Between 85% to 95% of peak network performance is achieved for all-to-all traffic, while over 85% of peak is obtained for challenging bisection pairings. A new software-controlled algorithm is developed for bisection traffic that selects which hardware algorithm to employ and achieves better performance than any individual hardware algorithm. The benefit of dynamic routing is shown for a highly non-uniform "transpose" traffic pattern. To evaluate memory and network performance, the HPCC Random Access benchmark was tuned for BG/Q and achieved 858 Giga Updates per Second (GUPS) on 16K nodes. To further accelerate message processing, the message libraries on BG/Q enable the offloading of messaging overhead onto dedicated communication threads. Several applications, including Algebraic Multigrid (AMG), exhibit from 3 to 20% gain using communication threads.

We present a novel performance monitor architecture, implemented in the Blue Gene/P™ supercomputer. This performance monitor supports the tracking of a large number of concurrent events by using a hybrid counter architecture. The counters have their low order data implemented in registers which are concurrently updated, while the high order counter data is maintained in a dense SRAM array that is updated from the registers on a regular basis. The per formance monitoring architecture includes support for per- event thresholding and fast event notification, using a two- phase interrupt-arming and triggering protocol. A first implementation provides 256 concurrent 64b counters which offers an up to 64x increase in counter number compared to performance monitors typically found in microprocessors today, and thereby dramatically expands the capabilities of counter-based performance tuning.

The Weather Research and Forecast (WRF) model is a limited-area model of the atmosphere for mesoscale research and operational numerical weather prediction (NWP). A petascale problem is a WRF nature run that provides very high-resolution "truth" against which more coarse simulations or perturbation runs may be compared for purposes of studying predictability, stochastic parameterization, and fundamental dynamics. We carried out a nature run involving an idealized high resolution rotating fluid on the hemisphere to investigate scales that span the k-3 to k-5/3 kinetic energy spectral transition of the observed atmosphere using 65,536 processors of the BG/L machine at LLNL. We worked through issues of parallel I/O and scalability. The primary result is not just the scalability and high Tflops number, but an important step towards understanding weather predictability at high resolution.

Applications on today's massively parallel supercomputers rely on performance analysis tools to guide them toward scalable performance on thousands of processors. However, conventional tools for parallel performance analysis have serious problems due to the large data volume that may be required. In this paper, we discuss the scalability issue for MPI performance analysis on Blue Gene/L, the world's fastest supercomputing platform. We present an experimental study of existing MPI performance tools that were ported to BG/L from other platforms. These tools can be classified into two categories: profiling tools that collect timing summaries, and tracing tools that collect a sequence of time-stamped events. Profiling tools produce small data volumes and can scale well, but tracing tools tend to scale poorly. The experimental study discusses the advantages and disadvantages for the tools in the two categories and will be helpful in the future performance tools design.

Applications on today's massively parallel supercomputers are often guided with performance analysis tools toward scalable performance on thousands of processors. However, conventional tools for parallel performance analysis have serious problems due to the large data volume that needs to be handled. In this paper, we discuss the scalability issue for MPI performance analysis on Blue Gene/L, the world's fastest supercomputing platform. First we present an experimental study of existing MPI performance tools that were ported to BG/L from other platforms. These tools can be classified into two categories: profiling tools that collect timing summaries, and tracing tools that collect a sequence of time-stamped events. Profiling tools produce small data volumes and can scale well, but tracing tools tend to scale poorly. We then describe a configurable MPI tracing tool developed for BG/L. By providing a configurable method for trace generation. the volume of trace data can be controlled, and scalability is significantly improved.

Blue Gene/L represents a new way to build supercomputers, using a large number of low power processors, together with multiple integrated interconnection networks. Whether real applications can scale to tens of thousands of processors (on a machine like Blue Gene/L) has been an open question. In this paper, we describe early experience with several physics and material science applications on a 32,768 node Blue Gene/L system, which was installed recently at the Lawrence Livermore National Laboratory. Our study shows some problems in the applications and in the current software implementation, but overall, excellent scaling of these applications to 32K nodes on the current Blue Gene/L system. While there is clearly room for improvement, these results represent the first proof point that MPI applications can effectively scale to over ten thousand processors. They also validate the scalability of the hardware and software architecture of Blue Gene/L.

The BlueGene/L supercomputer is expected to deliver new levels of application performance by providing a combination of good single-node computational performance and high scalability. To achieve good single-node performance, the BlueGene/L design includes a special dual floating-point unit on each processor and the ability to use two processors per node. BlueGene/L also includes both a torus and a tree network to achieve high scalability. We demonstrate how benchmarks and applications can take advantage of these architectural features to get the most out of BlueGene/L.