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GTC ON-DEMAND

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Abstract:
We present Unicorn, a novel parallel programming model for GPU clusters. It shows that distributed shared memory systems can be efficient with the help of transactional semantics and deferred data synchronizations, and thus the simplicity of distributed shared memory systems can be carried over to CPUs and GPUs in a cluster. Unicorn is designed for easy programmability and provides a deterministic execution environment. Device, node and cluster management are completely handled by the runtime and no related API is provided to the application programmer. Load balancing, scheduling and scalability are also fully transparent to the application code. Programs written on one cluster can be run verbatim on a different cluster. Application code is agnostic to data placement within the cluster as well as the changes in network interfaces and data availability pattern. Unicorn''s programming model, being deterministic, by design eliminates several data races and deadlocks. Unicorn''s runtime employs several data optimizations including prefetching and subtask streaming in order to overlap communication and computation. Unicorn employs pipelining at two levels first to hide data transfer costs among cluster nodes and second to hide transfer latency between CPUs and GPUs on all nodes. Among other optimizations, Unicorn''s work-stealing based scheduler employs a two-level victim selection technique to reduce the overhead of steal operations. Further, it employs special proactive and aggressive stealing mechanism to prevent the said pipelines from stalling (during a steal operation). We will showcase the scalability and performance of Unicorn on several scientific workloads. We will also demonstrate the load balancing achieved in some of these experiments and the amount of time the runtime spends in communications. We find that parallelization of coarse-grained applications like matrix multiplication or 2D FFT using our system requires only about 30 lines of C code to set up the runtime. The rest of the application code is regular single CPU/GPU implementation. This indicates the ease of extending sequential code to a parallel environment. We will be showing the efficiency of our abstraction with minimal loss on performance on latest GPU architecture like Pascal and Volta. Also we will be comparing our approach to other similar implementations like StarPU-MPI and G-Charm.
We present Unicorn, a novel parallel programming model for GPU clusters. It shows that distributed shared memory systems can be efficient with the help of transactional semantics and deferred data synchronizations, and thus the simplicity of distributed shared memory systems can be carried over to CPUs and GPUs in a cluster. Unicorn is designed for easy programmability and provides a deterministic execution environment. Device, node and cluster management are completely handled by the runtime and no related API is provided to the application programmer. Load balancing, scheduling and scalability are also fully transparent to the application code. Programs written on one cluster can be run verbatim on a different cluster. Application code is agnostic to data placement within the cluster as well as the changes in network interfaces and data availability pattern. Unicorn''s programming model, being deterministic, by design eliminates several data races and deadlocks. Unicorn''s runtime employs several data optimizations including prefetching and subtask streaming in order to overlap communication and computation. Unicorn employs pipelining at two levels first to hide data transfer costs among cluster nodes and second to hide transfer latency between CPUs and GPUs on all nodes. Among other optimizations, Unicorn''s work-stealing based scheduler employs a two-level victim selection technique to reduce the overhead of steal operations. Further, it employs special proactive and aggressive stealing mechanism to prevent the said pipelines from stalling (during a steal operation). We will showcase the scalability and performance of Unicorn on several scientific workloads. We will also demonstrate the load balancing achieved in some of these experiments and the amount of time the runtime spends in communications. We find that parallelization of coarse-grained applications like matrix multiplication or 2D FFT using our system requires only about 30 lines of C code to set up the runtime. The rest of the application code is regular single CPU/GPU implementation. This indicates the ease of extending sequential code to a parallel environment. We will be showing the efficiency of our abstraction with minimal loss on performance on latest GPU architecture like Pascal and Volta. Also we will be comparing our approach to other similar implementations like StarPU-MPI and G-Charm.  Back
 
Topics:
HPC and AI, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8565
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Speakers:
Subodh Kumar
Abstract:

We present our recently undergoing work to design and develop a GPU based unified modeling system for seamless weather and climate predictions of Monsoons. The system design is capable of handling different time and spatial scales of atmospheric phenomena that are crucial for accurate forecasting of weather and regional climates, and of monsoons in particular. Our focus is on high-resolution model utilizing accurate approximations on the icosahedral-hexagonal grid. We also develop parameterizations of fine and multi-scale moist convective processes, cloud microphysics and precipitation, radiative transfer, hydrology and land surface processes, atmospheric and oceanic turbulence. Starting with the core of LMDZ model, we are developing from scratch a parallel version appropriate for efficient computation on GPUs and CPUs. Another goal of our system design is to rid the programmer with low level programming details using a programming model that automatically distributes computation among all available CPUs and GPUs appropriately. We are developing a programming API to unify parallel code development on CPUs and GPUs.

We present our recently undergoing work to design and develop a GPU based unified modeling system for seamless weather and climate predictions of Monsoons. The system design is capable of handling different time and spatial scales of atmospheric phenomena that are crucial for accurate forecasting of weather and regional climates, and of monsoons in particular. Our focus is on high-resolution model utilizing accurate approximations on the icosahedral-hexagonal grid. We also develop parameterizations of fine and multi-scale moist convective processes, cloud microphysics and precipitation, radiative transfer, hydrology and land surface processes, atmospheric and oceanic turbulence. Starting with the core of LMDZ model, we are developing from scratch a parallel version appropriate for efficient computation on GPUs and CPUs. Another goal of our system design is to rid the programmer with low level programming details using a programming model that automatically distributes computation among all available CPUs and GPUs appropriately. We are developing a programming API to unify parallel code development on CPUs and GPUs.

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Topics:
Climate, Weather & Ocean Modeling
Type:
Talk
Event:
GTC China
Year:
2011
Session ID:
GTCA1174
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