4 research outputs found

    Heterogeneous CPU/GPU Memory Hierarchy Analysis and Optimization

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    In this master thesis, we propose a scheduling reordering for heterogeneous processors based on a hysteresis detector to give some fairness and speedup to the memory request threads taking advantage of the bank level parallelism at the memory system organization

    Functional verification of a RISC-V vector accelerator

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    We present the functional verification efforts for an academic RISC-V based vector accelerator, successfully taped-out in the context of the European Processor Initiative. For our novel RISC-V based decoupled vector accelerator, we built a verification infrastructure consisting of a UVM environment, performing step by step co-simulation of all vector instructions, using the Spike instruction set simulator as a reference model. Furthermore, for validating this complex design connected to a scalar core using a custom interface, we provided automated constrained-random test generation, simulation and error reporting, and CI/CD infrastructure. We found 3005 errors during this process and reached 95.79% functional coverage.This research has received funding from the European High Performance Computing Joint Undertaking (JU) under Framework Partnership Agreement No 800928 (European Processor Initiative) and Specific Grant Agreement No 101036168 (EPI SGA2). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and from Croatia, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, and Switzerland. The EPI-SGA2 project, PCI2022-132935 is also co-funded by MCIN/AEI /10.13039/501100011033 and by the UE NextGenerationEU/PRTR.Peer ReviewedPostprint (author's final draft

    Vitruvius+: An area-efficient RISC-V decoupled vector coprocessor for high performance computing applications

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    The maturity level of RISC-V and the availability of domain-specific instruction set extensions, like vector processing, make RISC-V a good candidate for supporting the integration of specialized hardware in processor cores for the High Performance Computing (HPC) application domain. In this article,1 we present Vitruvius+, the vector processing acceleration engine that represents the core of vector instruction execution in the HPC challenge that comes within the EuroHPC initiative. It implements the RISC-V vector extension (RVV) 0.7.1 and can be easily connected to a scalar core using the Open Vector Interface standard. Vitruvius+ natively supports long vectors: 256 double precision floating-point elements in a single vector register. It is composed of a set of identical vector pipelines (lanes), each containing a slice of the Vector Register File and functional units (one integer, one floating point). The vector instruction execution scheme is hybrid in-order/out-of-order and is supported by register renaming and arithmetic/memory instruction decoupling. On a stand-alone synthesis, Vitruvius+ reaches a maximum frequency of 1.4 GHz in typical conditions (TT/0.80V/25°C) using GlobalFoundries 22FDX FD-SOI. The silicon implementation has a total area of 1.3 mm2 and maximum estimated power of ~920 mW for one instance of Vitruvius+ equipped with eight vector lanes.This research has received funding from the European High Performance Computing Joint Undertaking (JU) under Framework Partnership Agreement No 800928 (European Processor Initiative) and Specific Grant Agreement No 101036168 (EPI SGA2). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and from Croatia, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, and Switzerland. The EPI-SGA2 project, PCI2022-132935 is also co-funded by MCIN/AEI/10.13039/501100011033 and by the UE NextGen- erationEU/PRTR. This work has also been partially supported by the Spanish Ministry of Science and Innovation (PID2019-107255GB-C21/AEI/10.13039/501100011033).Peer ReviewedPostprint (author's final draft

    Heterogeneous CPU/GPU Memory Hierarchy Analysis and Optimization

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    In this master thesis, we propose a scheduling reordering for heterogeneous processors based on a hysteresis detector to give some fairness and speedup to the memory request threads taking advantage of the bank level parallelism at the memory system organization
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