Graph Neural Networks (GNNs) have been successfully adopted in various application domains and accelerated by parallel processors such as GPUs. Despite the existence of popular frameworks such as Deep Graph Library (DGL) and PyTorch Geometric (PyG), the inconsistent programming paradigms and the lack of a unified analysis toolkit both hinder effective performance comparison among different GNN frameworks. This missing capability not only complicates the selection of the most suitable framework for users, but also impedes developers from optimizing framework implementations. In this paper, we propose GNNPerf, a performance profiling and analysis toolkit for effective performance comparison across GNN frameworks. GNNPerf provides a domain-specific language enabling unified GNN design expression and automatic generation to frameworkspecific implementations. GNNPerf also provides full workflow support for comprehensively evaluating GNN models with easy-to-use profiling, visualization, and analysis. The experimental results demonstrate that the GNNPerf can identify performance bottlenecks and empower users to derive actionable insights, enhancing both GNN model design and framework implementation.

MPI tracing tools is essential to collect the communication events and performance metrics of large-scale programs for further performance analysis and optimization. However, toward the exascale era, the performance and storage overhead for tracing becomes extremely prohibitive that significantly disturbs the original execution of MPI programs, leading to distorted tracing data and thus mislead analysis results. Although process sampling can effectively reduce the tracing overhead, it can easily miss important execution information that is necessary for subsequent performance analysis. In this article, we propose SimTrace , a scalable MPI tracing tool with novel spatial and temporal sampling strategies that exploits the similarity among MPI processes to achieve both low tracing overhead as well as obtain sufficient tracing information. The experimental results demonstrate that SimTrace can significantly reduce the MPI tracing overhead compared to the state-of-the-art tracing tools, meanwhile enabling effective analysis to guide performance optimization of large-scale programs.

Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constrains their practical adoption to identify performance inefficiencies. In this paper, we propose STAD, a performance analysis tool for parallel programs that considers both spatial and temporal patterns within trace data. STAD captures the spatial communication patterns between processes using a spatial communication pattern graph. It then adopts a dynamic graph neural network-based unsupervised model to learn the evolving temporal patterns along the timeline. Additionally, STAD diagnoses the root causes of performance anomalies by exploiting the aggregated feature of anomalies along the call tree. Our evaluation results demonstrate that STAD can effectively detect performance anomalies with acceptable overhead and diagnose the root causes attributed to both the program itself and the running environment.

As the performance gap between hardware and software widens, performance analysis tools are essential for understanding the behavior of large-scale High-Performance Computing (HPC) programs. These tools provide insights into the performance bottlenecks and help in optimizing the performance of the programs. In this paper, we present a comprehensive study of performance analysis tools for large-scale HPC systems including both sampling-based and instrumentation-based tools that are commonly adopted in the HPC community. We investigate the abundance and overheads of data collection as well as the analysis capabilities of HPCToolkit, TAU, and Scalasca with representative programs at scale. Our study shows that different performance analysis tools have distinct strengths and weaknesses, and the choice of a performance analysis tool depends on the specific requirements of the user. We also discuss the challenges and future directions in the field of performance analysis tools for large-scale HPC systems.

Performance variance is one of the nasty pitfalls of large-scale heterogeneous systems, which can lead to unexpected and unpredictable performance degradation for parallel programs. Such performance issues typically arise from various random hardware and software faults, making it exceedingly difficult to pinpoint the exact causes of performance variance in specific instances. In this paper, we propose GVARP, a performance variance detection tool for large-scale heterogeneous systems. GVARP employs static analysis to identify the performancecritical parameters of kernel functions. Additionally, GVARP segments the program execution with external library calls and asynchronous kernel operations. Then GVARP constructs a state transfer graph and estimates the workload of each program segment to identify and cluster instances of similar workloads, facilitating the detection of performance variance. Our evaluation results demonstrate that GVARP effectively detects performance variance at a large scale with acceptable overhead and provides intuitive insights to locate the sources of performance variance.

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement.

Energy efficiency has become one of the major concerns in high-performance computing systems towards exascale. On mainstream systems, dynamic voltage and frequency scaling (DVFS) and uncore frequency scaling (UFS) are two popular techniques to trade-off performance and power consumption to achieve better energy efficiency. However, the existing system software is oblivious to application characteristics and thus misses the opportunity for fine-grained power management. Meanwhile, manually instrumenting applications with power management codes are prohibitive due to heavy engineering efforts and thus hardly portable across platforms. In this paper, we propose Powerspector, a fine-grained code profiling and optimization tool with calling context awareness to automatically explore the opportunity for optimizing energy efficiency. The design of Powerspector consists of three phases, including significant region detection, performance profiling and power modeling, and frequency optimization. The first phase automatically identifies the profitable regions for frequency optimization. Then, the second phase guides the core/uncore frequency optimization with power models. The third phase injects frequency optimization codes targeting each significant code region across different calling contexts automatically. The experiment results demonstrate that Powerspector can achieve 1.13×(1.00×), 1.28×(1.09×), and 1.17×(1.06×) improvement on energy efficiency compared to static(region-based) tuning on Haswell, Broadwell, and Skylake platforms, respectively.