Understanding and managing public transportation systems require capturing complex spatio-temporal correlations within datasets. Existing studies often use predefined graphs in graph learning frameworks, neglecting shifted spatial and long-term temporal ...

Modern language runtimes use just-in-time compilation to execute applications natively. Typically, multiple compiler tiers cooperate so that compilation at a later stage can leverage profiling information generated by earlier tiers. This allows for ...

Using object lifetime information enables performance improvement through memory optimizations such as pretenuring and tuning garbage collector parameters. However, profiling object lifetimes is nontrivial and often requires a specialized virtual machine ...

Electric Vehicles (EVs) represent a green alternative to traditional fuel-powered vehicles. To enforce their widespread use, both the technical development and the security of users shall be guaranteed. Users’ privacy represents a possible threat that ...

Recent innovation in large language models (LLMs), and their myriad use cases have rapidly driven up the compute demand for datacenter GPUs. Several cloud providers and other enterprises plan to substantially grow their datacenter capacity to support ...

Generative AI models are at the forefront of advancing creative and analytical tasks, pushing the boundaries of what machines can generate and comprehend. Among these, latent diffusion models represent significant advancements in generating high-fidelity ...

Dynamic program performance analysis (also known as profiling) is well-known for its powerful capabilities of identifying performance inefficiencies in software packages. Although a large number of profiling techniques are developed in academia and ...

Recently Processing-in-Memory (PIM) has become a promising solution to achieve energy-efficient computation in data-intensive applications by placing computation near or inside the memory. In most Deep Learning (DL) frameworks, a user manually partitions ...

We present nnPerf, a real-time on-device profiler designed to collect and analyze the DNN model run-time inference latency on mobile platforms. nnPerf demystifies the hidden layers and metrics used for pursuing DNN optimizations and adaptations at the ...

In the context of the expanding landscape of contemporary High-Performance Computing (HPC) applications from petascale to exascale, the pursuit of performance optimization emerges as a significant impediment within software development endeavors. In the ...

Virtual reality (VR) telepresence applications and the so-called “metaverse” promise to be the next major medium of human-computer interaction. However, with recent studies demonstrating the ease at which VR users can be profiled and deanonymized, ...

Sound migratory typing envisions a safe and smooth refactoring of untyped code bases to typed ones. However, the cost of enforcing safety with run-time checks is often prohibitively high, thus performance regressions are a likely occurrence. ...

Efficiently retrieving the top-k documents for a given query is a fundamental operation in many search applications. Dynamic pruning algorithms accelerate top-k retrieval over inverted indexes by skipping documents that are not able to enter the current ...

Optimizing performance inefficiencies in memory hierarchies is well-known for native languages, such as C and C++. There are few studies, however, on exploring memory inefficiencies in Android Runtime (ART). Running in ART, managed languages, such as ...

Tracking specific events in a program’s execution, such as object allocation or lock acquisition, is at the heart of dynamic analysis. Despite the apparent simplicity of this task, quantifying these events is challenging due to the presence of compiler ...

Computing demand continues to grow exponentially, largely driven by "big data" processing on hyperscale data stores. At the same time, the slowdown in Moore's law is leading the industry to embrace custom computing in large-scale systems. Taken together, ...

General-purpose lossless data compression and decompression ("(de)compression") are used widely in hyperscale systems and are key "datacenter taxes". However, designing optimal hardware compression and decompression processing units ("CDPUs") is ...

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able ...

The embarrassingly parallel nature of deep learning training tasks makes CPU-GPU co-processors the primary commodity hardware for them. The computing and memory requirements of these tasks, however, do not always align well with the available GPU ...

In this paper, we present PerfoRT, a tool to ease software performance regression measurement of Java systems. Its main characteristics include: minimal configuration to ease automation and hide complexity to the end user; a broad scope of performance ...