218 research outputs found
Characterization and optimization of network traffic in cortical simulation
Considering the great variety of obstacles the Exascale systems
have to face in the next future, a deeper attention will be given in this thesis
to the interconnect and the power consumption.
The data movement challenge involves the whole hierarchical organization
of components in HPC systems — i.e. registers, cache, memory, disks.
Running scientific applications needs to provide the most effective methods
of data transport among the levels of hierarchy. On current petaflop systems,
memory access at all the levels is the limiting factor in almost all applications.
This drives the requirement for an interconnect achieving adequate rates of
data transfer, or throughput, and reducing time delays, or latency, between
the levels.
Power consumption is identified as the largest hardware research challenge.
The annual power cost to operate the system would be above 2.5 B$
per year for an Exascale system using current technology. The research for alternative
power-efficient computing device is mandatory for the procurement
of the future HPC systems.
In this thesis, a preliminary approach will be offered to the critical process of
co-design. Co-desing is defined as the simultaneos design of both hardware
and software, to implement a desired function. This process both integrates
all components of the Exascale initiative and illuminates the trade-offs that
must be made within this complex undertaking
APEnet+: a 3D toroidal network enabling Petaflops scale Lattice QCD simulations on commodity clusters
Many scientific computations need multi-node parallelism for matching up both
space (memory) and time (speed) ever-increasing requirements. The use of GPUs
as accelerators introduces yet another level of complexity for the programmer
and may potentially result in large overheads due to the complex memory
hierarchy. Additionally, top-notch problems may easily employ more than a
Petaflops of sustained computing power, requiring thousands of GPUs
orchestrated with some parallel programming model. Here we describe APEnet+,
the new generation of our interconnect, which scales up to tens of thousands of
nodes with linear cost, thus improving the price/performance ratio on large
clusters. The project target is the development of the Apelink+ host adapter
featuring a low latency, high bandwidth direct network, state-of-the-art wire
speeds on the links and a PCIe X8 gen2 host interface. It features hardware
support for the RDMA programming model and experimental acceleration of GPU
networking. A Linux kernel driver, a set of low-level RDMA APIs and an OpenMPI
library driver are available, allowing for painless porting of standard
applications. Finally, we give an insight of future work and intended
developments
Identification of novel human breast carcinoma (MDA-MB-231) Cell growth modulators from a carbohydrate-based diversity oriented synthesis library
The application of a cell-based growth inhibition on a library of skeletally different glycomimetics allowed for the selection of a hexahydro-2H-furo[3,2-b][1,4]oxazine compound as candidate inhibitors of MDA-MB-231 cell growth. Subsequent synthesis of analogue compounds and preliminary biological studies validated the selection of a valuable hit compound with a novel polyhydroxylated structure for the modulation of the breast carcinoma cell cycle mechanism
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