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GTC ON-DEMAND

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Abstract:

Learn how to make your irregular algorithm perform on GPUs. We provide insights into our research on a tasking framework for synchronization-critical applications on GPUs. We discuss the requirements of GPU architectures and programming models for implementing efficient tasking frameworks. Participants will learn about the pitfalls for tasking arising from the architectural differences between latency-driven CPUs and throughput-driven GPUs. To overcome these pitfalls, we consider programming concepts such as persistent threads, warp-aware data structures and CUDA asynchronous task graphs. In addition, we look at the latest GPU features such as forward progress guarantees and grid synchronization that facilitate the implementation of tasking approaches. A task-based fast multipole method for the molecular dynamics package GROMACS serves as use case for our considerations.

Learn how to make your irregular algorithm perform on GPUs. We provide insights into our research on a tasking framework for synchronization-critical applications on GPUs. We discuss the requirements of GPU architectures and programming models for implementing efficient tasking frameworks. Participants will learn about the pitfalls for tasking arising from the architectural differences between latency-driven CPUs and throughput-driven GPUs. To overcome these pitfalls, we consider programming concepts such as persistent threads, warp-aware data structures and CUDA asynchronous task graphs. In addition, we look at the latest GPU features such as forward progress guarantees and grid synchronization that facilitate the implementation of tasking approaches. A task-based fast multipole method for the molecular dynamics package GROMACS serves as use case for our considerations.

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Topics:
HPC and Supercomputing, Computational Biology & Chemistry, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9548
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Abstract:
We focus on a single code base for a certain scientific algorithm, a performance portable C++ implementation, using only a single code base that is easily executable in both CPU and GPU. For that purpose, we present our core algorithm -- the fast multipole method -- embedded in a stack of abstraction layers, allowing us to achieve portability without maintaining separate kernels for each architecture. In addition, we'll review common implementation pitfalls that might help other developers when aiming at a unified code base. Especially memory allocation, memory access, and the abstraction of SIMT for complex user-defined data structures are investigated. Finally, we present results/comparisons of the performance on a CPU and GPU.
We focus on a single code base for a certain scientific algorithm, a performance portable C++ implementation, using only a single code base that is easily executable in both CPU and GPU. For that purpose, we present our core algorithm -- the fast multipole method -- embedded in a stack of abstraction layers, allowing us to achieve portability without maintaining separate kernels for each architecture. In addition, we'll review common implementation pitfalls that might help other developers when aiming at a unified code base. Especially memory allocation, memory access, and the abstraction of SIMT for complex user-defined data structures are investigated. Finally, we present results/comparisons of the performance on a CPU and GPU.  Back
 
Topics:
Algorithms & Numerical Techniques, HPC and Supercomputing
Type:
Poster
Event:
GTC Silicon Valley
Year:
2016
Session ID:
P6265
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