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We present a framework for experimenting with secure multi-party computation directly in TensorFlow. By doing so we benefit from several properties valuable to both researchers and practitioners, including tight integration with ordinary…
Inverse transparency is created by making all usages of employee data visible to them. This requires tools that handle the logging and storage of usage information, and making logged data visible to data owners. For research and teaching…
Serverless computing offers an event driven pay-as-you-go framework for application development. A key selling point is the concept of no back-end server management, allowing developers to focus on application functionality. This is…
While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning…
On the way to Exascale, programmers face the increasing challenge of having to support multiple hardware architectures from the same code base. At the same time, portability of code and performance are increasingly difficult to achieve as…
Transparency - the provision of information about what personal data is collected for which purposes, how long it is stored, or to which parties it is transferred - is one of the core privacy principles underlying regulations such as the…
Within the last years, Python became more prominent in the scientific community and is now used for simulations, machine learning, and data analysis. All these tasks profit from additional compute power offered by parallelism and…
Many applications benefit from computations over the data of multiple users while preserving confidentiality. We present a solution where multiple mutually distrusting users' data can be aggregated with an acceptable overhead, while…
This report describes 1) how we use Intel's Optane DCPMM in the memory Mode. We investigate the the scalability of applications on a single Optane machine, using Subgraph counting as memory-intensive graph problem. We test with various…
Recent proliferation of embedded systems has generated a bold new paradigm, known as open embedded systems. While traditional embedded systems provide only closed base applications (natively-installed software) to users, open embedded…
Function-as-a-Service (FaaS) is at the core of serverless computing, enabling developers to easily deploy applications without managing computing resources. With an Infrastructure-as-Code (IaC) approach, frameworks like the Serverless…
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…
Existing Python libraries and tools lack the ability to efficiently compute statistical test results for large datasets in the presence of missing values. This presents an issue as soon as constraints on runtime and memory availability…
A processor's memory hierarchy has a major impact on the performance of running code. However, computing platforms, where the actual hardware characteristics are hidden from both the end user and the tools that mediate execution, such as a…
Parsl is a parallel programming library for Python that aims to make it easy to specify parallelism in programs and to realize that parallelism on arbitrary parallel and distributed computing systems. Parsl relies on developers annotating…
Dataflow programming is a popular and convenient programming paradigm in systems modelling, optimisation, and machine learning. It has a number of advantages, for instance the lacks of control flow allows computation to be carried out in…
In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications. This paper presents a comprehensive study of…
Modern applications demand high performance and cost efficient database management systems (DBMSs). Their workloads may be diverse, ranging from online transaction processing to analytics and decision support. The cloud infrastructure…
Cloud service provider propose services to insensitive customers to use their platform. Different services can achieve the same result at different cost. In this paper, we study the efficiency of a serverless architecture for running highly…
Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants can achieve complex functionality. Prior researches adopt the…