With the recent explosive growth in worldwide data and data processing demands, the need to support a large volume of transactions on shared data is increasing in both high performance computing and datacenter processing. A recent innovation in server architectures is the use of disaggregated memory based on the Compute eXpress Link (CXL) interconnect protocol. This memory architecture is increasing in popularity as it allows for dynamic demand-sensitive resizing of aggregated memory, support for heterogeneous memory types, and sharing of data amongst supported processors and devices, including computational accelerators. However, while this new memory architecture alleviates many concerns in datacenter architectures, the data integrity when using memory based transactions over CXL faces many challenges.To solve for these challenges, we describe a novel solution for providing ACID (Atomicity, Consistency, Isolation, Durability) transactions in a CXL-based disaggregated memory architecture. We call this solution Transactional CXL or TCXL. TCXL requires no changes to the existing processor microarchitectures and is implemented in a software library with a back-end controller that can be embedded in a CXL controller or as a stand alone CXL Device or implemented on a host.The transactions support persistent memory durable transactions and in-memory volatile transactions which can be in a pooled memory expansion for a single processor or shared amongst multiple processors. TCXL also supports processor based Hardware Transactional Memory (HTM) based transactions both on processor and over CXL. We evaluate TCXL by extending a CXL simulator and executing micro-benchmarks. In addition to gaining the benefits of using CXL, we show TCXL outperforms other approaches.
Reliable and uninterrupted operation is crucial in supercomputers, especially during failures or inconsistencies i.e., anomalies. In this paper, we present a federated adaptive Digital Twin (DT) framework, with a focus on enhancing anomaly detection - a critical aspect of modern data center management. Our DT continuously monitors key metrics, detects anomalies powered by AI, and dynamically adjusts its monitoring parameters to ensure optimal performance. Using a dashboard, our system provides real-time alarms and detailed visualizations of detected anomalies, along with real-time visualization and forecast for selected metrics. Through a series of experiments, we validate the effectiveness of our approach in maintaining operational reliability and promptly identifying potential anomalies within the data center.
We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.
In this paper, we present GPU-optimizations for an ice-sheet modeling code known as MPAS-Albany Land Ice (MALI). MALI is a C++ template code that leverages the Kokkos programming model for portability and the Trilinos library for data structures, nonlinear and linear solvers and optimization packages for ice-sheet simulations. Performance of the most expensive kernel is assessed via the Roofline model to highlight the potential for code improvement according to the underlying GPU architecture. We perform a collection of optimizations consisting of loop fusions, loop optimizations and local accumulation to productively and portably attain an overall speedup of 3× in either NVIDIA and AMD GPU. We analyze the performance gains using a time-oriented performance portability model based on time per invocation and GPU data movement. Results show an increment between 20% and 50% on the performance portability metric by improving data locality on the GPU kernels of a Stokes solver and highlights the importance of optimizing GPU-ported scientific applications to maximize memory bandwidth and minimize data movement on modern supercomputers.
To increase the dependability and portability of scientific data analysis workflows (DAWs), recent work has proposed contract-driven design of DAWs, providing verifiable expectations and obligations to ensure that tasks run in a proper environment and produce correct results. However, the specification of suitable contracts is still left to the discretion of DAW developers, imposing labor-intensive manual work which likely hampers the widespread adoption of contracts in scientific practice. We report about work-in-progress of developing a pipeline empowered by Large Language Models for automatically generating code contracts from logical workflow descriptions. We instantiate this pipeline within the workflow system Nextflow, and evaluate its contract generation capabilities in an experiment using real-world Nextflow modules. Our findings indicate that we generate a substantial amount of contracts serving as starting point for DAW developers. Our approach demonstrates potential in assisting domain scientists with contract-driven design of DAWs, laying the groundwork for its future adoption.
