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
