Optimizing the HPC Environment for Exascale Applications
DescriptionWith exascale workloads getting more complex, the optimization of the HPC environment, hardware and software, is getting much more challenging. On the hardware side, they still include an important part of computations as measured by Flops performance and memory bandwidth. They might also integrate a significant Data Analytics portion for applications such as digital twins. Recently Artificial Intelligence optimization techniques have been introduced. AI optimized Surrogate models may replace classical computations with neural networks, they boost the application performance, sometimes with an order of magnitude improvement, when run on AI optimized hardware components ( GPUs, IPUs…). Other applications, e.g. gene sequencing, might optimally use dataflow components such as FPGAs. An exascale hardware platform tightly integrates a large number of heterogenous nodes for computing, data processing, AI …. Since the denser the system architecture, the more efficient, HPC cabinets power consumption and heat dissipation requirements now far exceed classical IT systems air cooling capacity. We must rely on liquid cooling, which also allows for “free” cooling with high temperature water cooling (up-to 40°C ). In addition, the software environment provides an extra level of optimization with Machine Learning trained runtime modules for job scheduling, data instantiation, energy/performance control. Finally, there is a shift toward Cloud computing, either for financial reasons (OPEX vs CAPEX) or simply for providing extra resources on top of in-house systems. Once containerized (Docker) and managed by on orchestrator (Kubernetes) HPC applications run at bare-metal speed. The seamless integration of cloud and on-premises resources requires a federated management.
Presenter
Event Type
Exhibitor Forum
TimeThursday, 17 November 202211am - 11:30am CST
LocationD171
Recorded
TP
XO/EX