Abstract Certain workloads such asin‐memory databasesare inherently hard to scale‐out and rely on cache‐coherent scale‐up non‐uniform memory access (NUMA) systems to keep up with the ever‐increasing demand for compute resources. However, many parallel programming frameworks such as OpenMP do not make efficient use of large scale‐up NUMA systems as they do not consider data locality sufficiently. In this work, we presentPGASUS, a C++ framework for NUMA‐aware application development that provides integrated facilities for NUMA‐aware task parallelism and data placement. The framework is based on an extensive review of parallel programming languages and frameworks to incorporate the best practices of the field. In a comprehensive evaluation, we demonstrate thatPGASUSprovides average performance improvements of and peak performance improvements of up to across a wide range of workloads.

Data transfers impose a major bottleneck in heterogenous system architectures. As a mitigation strategy, compute resources can be introduced in places where data occurs naturally. The increased diversity of compute resources in turn affects programming models and practicalities of software development for near-data compute kernels and raises the question of how those resources can be made accessible to users and applications.

Cloud computing offers the potential to store, manage, and process data in highly available, scalable, and elastic environments. Yet, these environments still provide very limited and inflexible means for customers to control their data. For example, customers can neither specify security of inter-cloud communication bearing the risk of information leakage, nor comply with laws requiring data to be kept in the originating jurisdiction, nor control sharing of data with third parties on a fine-granular basis. This lack of control can hinder cloud adoption for data that falls under regulations. In this paper, we show in six use cases how cloud environments can be enriched with policy language support to give customers control over cloud data. Our use cases are based on realizing policy language support in all three cloud environment layers, i.e., IaaS, PaaS, and SaaS. Specifically, we present policy-aware resource management (with OpenStack) and dynamic network configuration. With CERN's big data storage and the in-memory database Hyrise, we show realization for storage and further exemplify policy-aware cloud processing by network function virtualization which enables Orange to offload customer home gateways to the cloud. Finally, we discuss benefits of policy support in F-Secure's Security Cloud. These use cases show the feasibility of realizing customer control with policy support in the cloud. Thus, our work enables customers with regulated data to tap cloud benefits and significantly broadens the market for cloud providers.