In computer architecture research and development, simulation is a powerful way of
acquiring and predicting processor behaviors. While architectural simulation has been …

Simulating all threads in a scaled GPU workload results in prohibitive simulation cost. Cycle-
level simulation is orders of magnitude slower than native silicon, the only solution is to …

GPUs, due to their massively-parallel computing architectures, provide high performance for
data-parallel applications. However, existing GPU simulators are too slow to enable …

Recent years have witnessed an increasing growth in the usage of GPUs in cloud data
centers. It is known that conventional virtualization techniques are not directly applicable to …

This paper proposes a hybrid framework for fast and accurate performance estimation of
OpenCL kernels running on GPUs. The kernel execution flow is statically analyzed and …

To exploit the ever increasing compute capabilities offered by GPU hardware, GPU-compute
workloads have evolved from simple computational kernels to large-scale programs with …

Analytical models can greatly help computer architects perform orders of magnitude faster
early-stage design space exploration than using cycle-level simulators. To facilitate rapid …

Architecture simulation for GPGPU kernels can take a significant amount of time, especially
for large-scale GPGPU kernels. This paper presents TBPoint, an infrastructure based on …

This paper introduces a predictive modeling framework to estimate the performance of GPUs
during pre-silicon design. Early-stage performance prediction is useful when simulation …

Graphic Processing Units (GPUs) play a vital role in state-of-the-art high-performance
scientific computing realm and research work towards its performance analysis is crucial but …