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Martin Schulz

Publications and source records attributed to Martin Schulz.

At least 37 records · Page 2Linked to original sources

VersaSlot: Efficient Fine-grained FPGA Sharing with Big.Little Slots and Live Migration in FPGA Cluster

As FPGAs gain popularity for on-demand application acceleration in data center computing, dynamic partial reconfiguration (DPR) has become an effective fine-grained sharing technique for FPGA multiplexing. However, current FPGA sharing encounters partial reconfiguration contention and task execution blocking problems introduced by the DPR, which significantly degrade application performance. In this paper, we propose VersaSlot, an efficient spatio-temporal FPGA sharing system with novel Big{.}Little slot architecture that can effectively resolve the contention and task blocking while improving resource utilization. For the heterogeneous Big{.}Little architecture, we introduce an efficient slot allocation and scheduling algorithm, along with a seamless cross-board switching and live migration mechanism, to maximize FPGA multiplexing across the cluster. We evaluate the VersaSlot system on an FPGA cluster composed of the latest Xilinx UltraScale+ FPGAs (ZCU216) and compare its performance against four existing scheduling algorithms. The results demonstrate that VersaSlot achieves up to 13.66x lower average response time than the traditional temporal FPGA multiplexing, and up to 2.19x average response time improvement over the state-of-the-art spatio-temporal sharing systems. Furthermore, VersaSlot enhances the LUT and FF resource utilization by 35% and 29% on average, respectively.

cs.DC↗

Modeling the Potential of Message-Free Communication via CXL.mem

Heterogeneous memory technologies are increasingly important instruments in addressing the memory wall in HPC systems. While most are deployed in single node setups, CXL.mem is a technology that implements memories that can be attached to multiple nodes simultaneously, enabling shared memory pooling. This opens new possibilities, particularly for efficient inter-node communication. In this paper, we present a novel performance evaluation toolchain combined with an extended performance model for message-based communication, which can be used to predict potential performance benefits from using CXL.mem for data exchange. Our approach analyzes data access patterns of MPI applications: it analyzes on-node accesses to/from MPI buffers, as well as cross-node MPI traffic to gather a full understanding of the impact of memory performance. We combine this data in an extended performance model to predict which data transfers could benefit from direct CXL.mem implementations as compared to traditional MPI messages. Our model works on a per-MPI call granularity, allowing the identification and later optimizations of those MPI invocations in the code with the highest potential for speedup by using CXL.mem. For our toolchain, we extend the memory trace sampling tool Mitos and use it to extract data access behavior. In the post-processing step, the raw data is automatically analyzed to provide performance models for each individual MPI call. We validate the models on two sample applications -- a 2D heat transfer miniapp and the HPCG benchmark -- and use them to demonstrate their support for targeted optimizations by integrating CXL.mem.

cs.DC↗

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Understanding GPU topology is essential for performance-related tasks in HPC or AI. Yet, unlike for CPUs with tools like hwloc, GPU information is hard to come by, incomplete, and vendor-specific. In this work, we address this gap and present MT4G, an open-source and vendor-agnostic tool that automatically discovers GPU compute and memory topologies and configurations, including cache sizes, bandwidths, and physical layouts. MT4G combines existing APIs with a suite of over 50 microbenchmarks, applying statistical methods, such as the Kolmogorov-Smirnov test, to automatically and reliably identify otherwise programmatically unavailable topological attributes. We showcase MT4G's universality on ten different GPUs and demonstrate its impact through integration into three workflows: GPU performance modeling, GPUscout bottleneck analysis, and dynamic resource partitioning. These scenarios highlight MT4G's role in understanding system performance and characteristics across NVIDIA and AMD GPUs, providing an automated, portable solution for modern HPC and AI systems.

cs.DC↗

Tackling the Challenges of Adding Pulse-level Support to a Heterogeneous HPCQC Software Stack: MQSS Pulse

We study the problem of adding native pulse-level control to heterogeneous High Performance Computing-Quantum Computing (HPCQC) software stacks, using the Munich Quantum Software Stack (MQSS) as a case study. The goal is to expand the capabilities of HPCQC environments by offering the ability for low-level access and control, currently typically not foreseen for such hybrid systems. For this, we need to establish new interfaces that integrate such pulse-level control into the lower layers of the software stack, including the need for proper representation. Pulse-level quantum programs can be fully described with only three low-level abstractions: ports (input/output channels), frames (reference signals), and waveforms (pulse envelopes). We identify four key challenges to represent those pulse abstractions at: the user-interface level, at the compiler level (including the Intermediate Representation (IR)), and at the backend-interface level (including the appropriate exchange format). For each challenge, we propose concrete solutions in the context of MQSS. These include introducing a compiled (C/C++) pulse Application Programming Interface (API) to overcome Python runtime overhead, extending its LLVM support to include pulse-related instructions, using its C-based backend interface to query relevant hardware constraints, and designing a portable exchange format for pulse sequences. Our integrated approach provides an end-to-end path for pulse-aware compilation and runtime execution in HPCQC environments. This work lays out the architectural blueprint for extending HPCQC integration to support pulse-level quantum operations without disrupting state-of-the-art classical workflows.

quant-ph↗

HiPARS: Highly-Parallel Atom Rearrangement Sequencer

Neutral atom quantum computing's great scaling potential has resulted in it emerging as a popular modality in recent years. For state preparation, atoms are loaded stochastically and have to be detected and rearranged at runtime to create a predetermined initial configuration for circuit execution. Such rearrangement schemes either suffer from low parallelizability for acousto-optic deflector (AOD)-based approaches or are comparatively slow in case of spatial light modulators (SLMs). In our work, we introduce an algorithm that can improve the parallelizability of the former. Since the transfer of atoms from static SLM traps to AOD-generated movable traps is detrimental both in terms of atom loss rates and execution time, our approach is based on highly-parallel composite moves where many atoms are picked up simultaneously and maneuvered into target positions that may be comparatively distant. We see that our algorithm outperforms its alternatives for near-term devices with up to around 1000 qubits and has the potential to scale up to several thousand with further optimizations.

cs.ET↗

Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors

Quantum computing in supercomputing centers requires robust tools to analyze calibration datasets, predict hardware performance, and optimize operational workflows. This paper presents a data-driven framework for processing calibration metrics. Our model is based on a real calibration quality metrics dataset from our in-house 20-qubit NISQ device and for more than 250 days. We apply detailed data analysis to uncover temporal patterns and cross-metric correlations. Using unsupervised clustering, we identify stable and noisy qubits. We also validate our model using GHZ state experiments. Our study provides health indicators as well as hardware-driven maintenance and recalibration recommendations, thus motivating the integration of relevant schedulers with HPCQC workflows.

quant-ph↗

Closing the HPC-Cloud Convergence Gap: Multi-Tenant Slingshot RDMA for Kubernetes

Converged HPC-Cloud computing is an emerging computing paradigm that aims to support increasingly complex and multi-tenant scientific workflows. These systems require reconciliation of the isolation requirements of native cloud workloads and the performance demands of HPC applications. In this context, networking hardware is a critical boundary component: it is the conduit for high-throughput, low-latency communication and enables isolation across tenants. HPE Slingshot is a high-speed network interconnect that provides up to 200 Gbps of throughput per port and targets high-performance computing (HPC) systems. The Slingshot host software, including hardware drivers and network middleware libraries, is designed to meet HPC deployments, which predominantly use single-tenant access modes. Hence, the Slingshot stack is not suited for secure use in multi-tenant deployments, such as converged HPC-Cloud deployments. In this paper, we design and implement an extension to the Slingshot stack targeting converged deployments on the basis of Kubernetes. Our integration provides secure, container-granular, and multi-tenant access to Slingshot RDMA networking capabilities at minimal overhead.

cs.DC↗

Q-BEAST: A Practical Course on Experimental Evaluation and Characterization of Quantum Computing Systems

Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and hands-on experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.

physics.ed-ph↗

FERIVer: An FPGA-assisted Emulated Framework for RTL Verification of RISC-V Processors

Processor design and verification require a synergistic approach that combines instruction-level functional simulations with precise hardware emulations. The trade-off between speed and accuracy in the instruction set simulation poses a significant challenge to the efficiency of processor verification. By tapping the potentials of Field Programmable Gate Arrays (FPGAs), we propose an FPGA-assisted System-on-Chip (SoC) platform that facilitates cross-verification by the embedded CPU and the synthesized hardware in the programmable fabrics. This method accelerates the verification of the RISC-V Instruction Set Architecture (ISA) processor at a speed of 5 million instructions per second (MIPS), which is 150x faster than the vendor-specific tool (Xilinx XSim) and a 35x boost to the state-of-the-art open-source verification setup (Verilator). With less than 7\% hardware occupation on Zynq 7000 FPGA, the proposed framework enables flexible verification with high time and cost efficiency for exploring RISC-V instruction set architectures.

cs.AR↗

KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA

Superconducting qubits are among the most promising candidates for building quantum information processors. Yet, they are often limited by slow and error-prone qubit readout -- a critical factor in achieving high-fidelity operations. While current methods, including deep neural networks, enhance readout accuracy, they typically lack support for mid-circuit measurements essential for quantum error correction, and they usually rely on large, resource-intensive network models. This paper presents KLiNQ, a novel qubit readout architecture leveraging lightweight neural networks optimized via knowledge distillation. Our approach achieves around a 99% reduction in model size compared to the baseline while maintaining a qubit-state discrimination accuracy of 91%. KLiNQ facilitates rapid, independent qubit-state readouts that enable mid-circuit measurements by assigning a dedicated, compact neural network for each qubit. Implemented on the Xilinx UltraScale+ FPGA, our design can perform the discrimination within 32ns. The results demonstrate that compressed neural networks can maintain high-fidelity independent readout while enabling efficient hardware implementation, advancing practical quantum computing.

quant-ph↗

Design of an FPGA-Based Neutral Atom Rearrangement Accelerator for Quantum Computing

Neutral atoms have emerged as a promising technology for implementing quantum computers due to their scalability and long coherence times. However, the execution frequency of neutral atom quantum computers is constrained by image processing procedures, particularly the assembly of defect-free atom arrays, which is a crucial step in preparing qubits (atoms) for execution. To optimize this assembly process, we propose a novel quadrant-based rearrangement algorithm that employs a divide-and-conquer strategy and also enables the simultaneous movement of multiple atoms, even across different columns and rows. We implement the algorithm on FPGA to handle each quadrant independently (hardware-level optimization) while maximizing parallelization. To the best of our knowledge, this is the first hardware acceleration work for atom rearrangement, and it significantly reduces the processing time. This achievement also contributes to the ongoing efforts of tightly integrating quantum accelerators into High-Performance Computing (HPC) systems. Tested on a Zynq RFSoC FPGA at 250 MHz, our hardware implementation is able to complete the rearrangement process of a 30$\times$30 compact target array, derived from a 50$\times$50 initial loaded array, in approximately 1.0 $μs$. Compared to a comparable CPU implementation and to state-of-the-art FPGA work, we achieved about 54$\times$ and 300$\times$ speedups in the rearrangement analysis time, respectively. Additionally, the FPGA-based acceleration demonstrates good scalability, allowing for seamless adaptation to varying sizes of the atom array, which makes this algorithm a promising solution for large-scale quantum systems.

quant-ph↗

OpenCUBE: Building an Open Source Cloud Blueprint with EPI Systems

OpenCUBE aims to develop an open-source full software stack for Cloud computing blueprint deployed on EPI hardware, adaptable to emerging workloads across the computing continuum. OpenCUBE prioritizes energy awareness and utilizes open APIs, Open Source components, advanced SiPearl Rhea processors, and RISC-V accelerator. The project leverages representative workloads, such as cloud-native workloads and workflows of weather forecast data management, molecular docking, and space weather, for evaluation and validation.

cs.DC↗

Comparison of Atom Detection Algorithms for Neutral Atom Quantum Computing

In neutral atom quantum computers, readout and preparation of the atomic qubits are usually based on fluorescence imaging and subsequent analysis of the acquired image. For each atom site, the brightness or some comparable metric is estimated and used to predict the presence or absence of an atom. Across different setups, we can see a vast number of different approaches used to analyze these images. Often, the choice of detection algorithm is either not mentioned at all or it is not justified. We investigate several different algorithms and compare their performance in terms of both precision and execution run time. To do so, we rely on a set of synthetic images across different simulated exposure times with known occupancy states. Since the use of simulation provides us with the ground truth of atom site occupancy, we can easily state precise error rates and variances of the reconstructed property. To also rule out the possibility of better algorithms existing, we calculated the Cramér-Rao bound in order to establish an upper limit that even a perfect estimator cannot outperform. As the metric of choice, we used the number of photonelectrons that can be contributed to a specific atom site. Since the bound depends on the occupancy of neighboring sites, we provide the best and worst cases, as well as a half filled one. Our comparison shows that of our tested algorithms, a global non-linear least-squares solver that uses the optical system's PSF to return a each sites' number of photoelectrons performed the best, on average crossing the worst-case bound for longer exposure times. Its main drawback is its huge computational complexity and, thus, required calculation time. We manage to somewhat reduce this problem, suggesting that its use may be viable. However, our study also shows that for cases where utmost speed is required, simple algorithms may be preferable.

quant-ph↗

Integration of Quantum Accelerators into HPC: Toward a Unified Quantum Platform

To harness the power of quantum computing (QC) in the near future, tight and efficient integration of QC with high performance computing (HPC) infrastructure (both on the software (SW) and the hardware (HW) level) is crucial. This paper addresses the development of a unified quantum platform (UQP) and how it is being integrated into the HPC ecosystem. It builds on the concepts of hybrid high performance computing - quantum computing (HPCQC) workflows and a unified HPCQC toolchain, introduced in our previous work and makes the next needed step: it unifies the low-level interface between the existing classical HPC systems and the emerging quantum hardware technologies, including but not limited to machines based on superconducting qubits, neutral atoms or trapped ions. The UQP consists of three core components: a runtime library, an instruction set architecture (ISA) and a quantum control processor (QCP) micro-architecture. In particular, this work contributes a unified HPCQC runtime library that bridges the gap between programming systems built on quantum intermediate representation (QIR) standard with a novel, unified hybrid ISA. It then introduces the initial extension of an ISA and QCP micro-architecture to be platform and technology agnostic and enables it as an efficient execution platform. The UQP has been verified to ensure correctness. Further, our performance analysis shows that the execution time and memory requirements of the runtime library scale super-linearly with number of qubits, which is critical to support scalability efforts in QC hardware.

cs.ET↗

Dataset Distillation by Automatic Training Trajectories

Dataset Distillation is used to create a concise, yet informative, synthetic dataset that can replace the original dataset for training purposes. Some leading methods in this domain prioritize long-range matching, involving the unrolling of training trajectories with a fixed number of steps (NS) on the synthetic dataset to align with various expert training trajectories. However, traditional long-range matching methods possess an overfitting-like problem, the fixed step size NS forces synthetic dataset to distortedly conform seen expert training trajectories, resulting in a loss of generality-especially to those from unencountered architecture. We refer to this as the Accumulated Mismatching Problem (AMP), and propose a new approach, Automatic Training Trajectories (ATT), which dynamically and adaptively adjusts trajectory length NS to address the AMP. Our method outperforms existing methods particularly in tests involving cross-architectures. Moreover, owing to its adaptive nature, it exhibits enhanced stability in the face of parameter variations.

cs.CV↗

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much higher computational throughput and memory bandwidth than CPUs in the same generation do. However, as the available resources in GPUs have increased exponentially over the past decades, it has become increasingly difficult for a single program to fully utilize them. As a consequence, the industry has started supporting several resource partitioning features in order to improve the resource utilization by co-scheduling multiple programs on the same GPU die at the same time. Driven by the technological trend, this paper focuses on hierarchical resource partitioning on modern GPUs, and as an example, we utilize a combination of two different features available on recent NVIDIA GPUs in a hierarchical manner: MPS (Multi-Process Service), a finer-grained logical partitioning; and MIG (Multi-Instance GPU), a coarse-grained physical partitioning. We propose a method for comprehensively co-optimizing the setup of hierarchical partitioning and the selection of co-scheduling groups from a given set of jobs, based on reinforcement learning using their profiles. Our thorough experimental results demonstrate that our approach can successfully set up job concurrency, partitioning, and co-scheduling group selections simultaneously. This results in a maximum throughput improvement by a factor of 1.87 compared to the time-sharing scheduling.

cs.DC↗

Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning

CPU-GPU heterogeneous architectures are now commonly used in a wide variety of computing systems from mobile devices to supercomputers. Maximizing the throughput for multi-programmed workloads on such systems is indispensable as one single program typically cannot fully exploit all available resources. At the same time, power consumption is a key issue and often requires optimizing power allocations to the CPU and GPU while enforcing a total power constraint, in particular when the power/thermal requirements are strict. The result is a system-wide optimization problem with several knobs. In particular we focus on (1) co-scheduling decisions, i.e., selecting programs to co-locate in a space sharing manner; (2) resource partitioning on both CPUs and GPUs; and (3) power capping on both CPUs and GPUs. We solve this problem using predictive performance modeling using machine learning in order to coordinately optimize the above knob setups. Our experiential results using a real system show that our approach achieves up to 67% of speedup compared to a time-sharing-based scheduling with a naive power capping that evenly distributes power budgets across components.

cs.DC↗

On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems

Recent High-Performance Computing (HPC) systems are facing important challenges, such as massive power consumption, while at the same time significantly under-utilized system resources. Given the power consumption trends, future systems will be deployed in an over-provisioned manner where more resources are installed than they can afford to power simultaneously. In such a scenario, maximizing resource utilization and energy efficiency, while keeping a given power constraint, is pivotal. Driven by this observation, in this position paper we first highlight the recent trends of resource management techniques, with a particular focus on malleability support (i.e., dynamically scaling resource allocations/requirements for a job), co-scheduling (i.e., co-locating multiple jobs within a node), and power management. Second, we consider putting them together, assess their relationships/synergies, and discuss the functionality requirements in each software component for future over-provisioned and power-constrained HPC systems. Third, we briefly introduce our ongoing efforts on the integration of software tools, which will ultimately lead to the convergence of malleability and power management, as it is designed in the HPC PowerStack initiative.

cs.DC↗