12 research outputs found
High Performance Code Generation for Stencil Computation on Heterogeneous Multi-device Architectures
International audienceHeterogeneous architectures have been widely used in the domain of high performance computing. On one hand, it allows a designer to use multiple types of computing units and each able to execute the tasks that it is best suited for to increase performance; on the other hand, it brings many challenges in programming for novice users, especially for heterogeneous systems with multi-devices. In this paper, we propose the code generator STEPOCL that generates OpenCL host program for heterogeneous multi-device architecture. In order to simplify the analyzing process, we ask user to provide the description of input and kernel parameters in an XML file, then our generator analyzes the description and generates automatically the host program. Due to the data partition and data exchange strategies, the generated host program can be executed on multi-devices without changing any kernel code. The experiment of iterative stencil loop code (ISL) shows that our tool is efficient. It guarantees the minimum data exchanges and achieves high performance on heterogeneous multi-device architecture
AN5D: Automated Stencil Framework for High-Degree Temporal Blocking on GPUs
Stencil computation is one of the most widely-used compute patterns in high
performance computing applications. Spatial and temporal blocking have been
proposed to overcome the memory-bound nature of this type of computation by
moving memory pressure from external memory to on-chip memory on GPUs. However,
correctly implementing those optimizations while considering the complexity of
the architecture and memory hierarchy of GPUs to achieve high performance is
difficult. We propose AN5D, an automated stencil framework which is capable of
automatically transforming and optimizing stencil patterns in a given C source
code, and generating corresponding CUDA code. Parameter tuning in our framework
is guided by our performance model. Our novel optimization strategy reduces
shared memory and register pressure in comparison to existing implementations,
allowing performance scaling up to a temporal blocking degree of 10. We achieve
the highest performance reported so far for all evaluated stencil benchmarks on
the state-of-the-art Tesla V100 GPU
Libra.Net: Single Task Scheduling in a CPU-GPU Heterogeneous Environment
In this thesis we developed a single task scheduler in a CPU-GPU heterogeneous environment.
We formulated a GPGPU performance model recognizing a ground model common to any GPGPU platform that must be refined to consider specific platforms. We proposed a model refinement for the Nvidia CUDA platform.
Moreover, we formulated a CPU performance model for the Common Language Infrastructure virtual execution environment.
Finally, we developed Libra.Net, a particular implementation of the scheduler for the Microsoft Common Language Runtime and evaluated its efficiency