International audienceGeneral purpose (GP)GPU programming demands to couple highly parallel computing units with classic CPUs to obtain a high performance. Heterogenous systems lead to complex designs combining multiple paradigms and programming languages to manage each hardware architecture. In this paper, we present tools to harness GPGPU programming through the high-level OCaml programming language. We describe the SPOC library that allows to handle GPGPU subprograms (kernels) and data transfers between devices. We then present how SPOC expresses GPGPU kernel: through interoperability with common low-level extensions (from Cuda and OpenCL frameworks) but also via an embedded DSL for OCaml. Using simple benchmarks as well as a real world HPC software, we show that SPOC can offer a high performance while efficiently easing development. To allow better abstractions over tasks and data, we introduce some parallel skeletons built upon SPOC as well as composition constructs over those skeletons
General purpose computing on graphics processing units (GPGPU) consists of using GPUs to handle computations commonly handled by CPUs. GPGPU programming implies developing specific programs to run on GPUs managed by a host program running on the CPU. To achieve high performance implies to explicitly organize memory transfers between devices. Besides, different incompatible frameworks exist making productivity and portability difficult to achieve. In this paper, we describe SPOC, an OCaml library, defining specific data sets in order to automatically manage transfers between GPU and CPU. SPOC also offers a runtime library looking for multiple frameworks and making them usable transparently. We also describe the link between SPOC and the OCaml garbage collector to optimize transfers dynamically. SPOC benchmarks show that SPOC can offer great performance while simplifying GPGPU programming
International audienceWe present a general model allowing static analysis based on abstract interpretation for systems of communicating processes. Our technique, inspired by Regular Model Checking, represents set of program states as lattice automata and programs semantics as symbolic transducers. This model can express dynamic creation/destruction of processes and communications. Using the abstract interpretation framework, we are able to provide a sound over-approximation of the reachability set of the system thus allowing us to prove safety properties. We implemented this method in a prototype that targets the MPI library for C programs
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