Reducing CPU-GPU Interferences to Improve CPU Performance in Heterogeneous Architectures

Performance Analysis of CPU-GPU Cluster Architectures

High performance computing (HPC) encompasses advanced computation over parallel processing, enabling faster execution of highly compute intensive tasks such as climate research, molecular modeling, physical simulations, cryptanalysis, geophysical modeling, automotive and aerospace design, financial modeling, data mining and more. High performance simulations require the most efficient compute p...

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Convolutional Neural Networks (CNNs) have shown to be powerful classification tools in tasks that range from check reading to medical diagnosis, reaching close to human perception, and in some cases surpassing it. However, the problems to solve are becoming larger and more complex, which translates to larger CNNs, leading to longer training times—the computational complex part—that not even the...

GPU-to-CPU Callbacks

We present GPU-to-CPU callbacks, a new mechanism and abstraction for GPUs that offers them more independence in a hetero-ion for GPUs that offers them more independence in a heterogeneous computing environment. Specifically, we provide a method for GPUs to issue callback requests to the CPU. These requests serve as a tool for ease-of-use, future proofing of code, and new functionality. We class...

Runtime specialization for heterogeneous CPU-GPU platforms

Cache optimization for CPU - GPU heterogeneous processors ∗

Microprocessors combining CPU and GPU cores using a common last-level cache pose new challenges to cache management algorithms. Since GPU cores feature much higher data access rates than CPU cores, the majority of the available cache space will be used by GPU applications, leaving only very limited cache capacity for CPU applications, which may be disadvantageous for overall system performance....