Data diffusion architectures (also known as COMA machines) are scalable multiprocessors
that provide a shared address space on top of distributed main memory. Their distinctive …

We present the design and evaluation of a Datalog engine for execution in Graphics Processing
Units (GPUs). The engine evaluates recursive and non-recursive Datalog queries using …

This paper reports the work in progress towards the specification of a conceptual architecture
of a smart system for supporting the management of disruptions in the manufacturing …

The data diffusion space (DDS) is an all-software shared address space for parallel computing
on distributed memory platforms. It is an extra address space to that of each process …

Writing parallel programs that can take advantage of non-dedicated processors is much
more difficult than writing such programs for networks of dedicated processors. In a non-…

Local area networks are now widely used to run parallel applications. They are particularly
suitable to run I/O intensive applications, because each node usually includes disk space. …

Relational learning algorithms mine complex databases for interesting patterns. Usually, the
search space of patterns grows very quickly with the increase in data size, making it …

Data diffusion architectures (also known as cache only memory architectures) provide a shared
address space on top of distributed memory. Their distinctive feature is that data diffuses, …

This paper presents compiler and run-time support that simplifies the programming of
adaptive parallel matrix computing. Matrices are declared with special keywords and can be …

Data-driven decision making is at the core of Industry 4.0. This paper describes the specification
of a conceptual architecture of a smart system for supporting decision making in the …