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In a functional language, the dominant control-flow mechanism is function call and return. Most higher-order flow analyses, including k-CFA, do not handle call and return well: they remember only a bounded number of pending calls because…
Recent years have seen an increased interest in large-scale analytical dataflows on non-relational data. These dataflows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant…
We introduce a new method for detecting scaling in time series. The method uses the properties of the probability flux for stochastic self-affine processes and is called the probability flux analysis (PFA). The advantages of this method…
Understanding and visualizing the full-stack performance trade-offs and interplay between HPC applications, MPI libraries, the communication fabric, and the file system is a challenging endeavor. Designing a holistic profiling and…
Through the increasing interconnection between various systems, the need for confidential systems is increasing. Confidential systems share data only with authorized entities. However, estimating the confidentiality of a system is complex,…
This dissertation proves lower bounds on the inherent difficulty of deciding flow analysis problems in higher-order programming languages. We give exact characterizations of the computational complexity of 0CFA, the $k$CFA hierarchy, and…
As models become larger, ML accelerators are a scarce resource whose performance must be continually optimized to improve efficiency. Existing performance analysis tools are coarse grained, and fail to capture model performance at the…
High-quality data is crucial for the pre-training performance of large language models. Unfortunately, existing quality filtering methods rely on a known high-quality dataset as reference, which can introduce potential bias and compromise…
Developing efficient parallel applications is critical to advancing scientific development but requires significant performance analysis and optimization. Performance analysis tools help developers manage the increasing complexity and scale…
Computer architecture design space is vast and complex. Tools are needed to explore new ideas and gain insights quickly, with low efforts and at a desired accuracy. We propose Calipers, a criticality-based framework to model key…
With weather becoming more extreme both in terms of longer dry periods and more severe rain events, municipal water networks are increasingly under pressure. The effects include damages to the pipes, flash floods on the streets and combined…
Flow Matching and Transformer architectures have demonstrated remarkable performance in image generation tasks, with recent work FlowAR [Ren et al., 2024] synergistically integrating both paradigms to advance synthesis fidelity. However,…
It is important for big data systems to identify their performance bottleneck. However, the popular indicators such as resource utilizations, are often misleading and incomparable with each other. In this paper, a novel indicator framework…
In GPU-accelerated data analytics, the overhead of data transfer from CPU to GPU becomes a performance bottleneck when the data scales beyond GPU memory capacity due to the limited PCIe bandwidth. Data compression has come to rescue for…
Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and…
As applications grow in capability, they also grow in complexity. This complexity in turn gets pushed into modules and libraries. In addition, hardware configurations become increasingly elaborate, too. These two trends make understanding,…
This paper proposes Scalene, a profiler specialized for Python. Scalene combines a suite of innovations to precisely and simultaneously profile CPU, memory, and GPU usage, all with low overhead. Scalene's CPU and memory profilers help…
This paper presents a stream-oriented architecture for structuring cluster applications. Clusters that run applications based on this architecture can scale to tenths of thousands of nodes with significantly less performance loss or…
The never-ending computational demand from simulations of turbulence makes computational fluid dynamics (CFD) a prime application use case for current and future exascale systems. High-order finite element methods, such as the spectral…
This demo showcases a platform for developing human activity recognition (AR) systems, focusing on daily activities using sensor data, like binary sensors. With a data-driven approach, this platform, named FlowAR, features a three-step…