Search for: All records

With the rapid advance in Deep Neural Networks (DNNs), GPU’s role as a hardware accelerator becomes increasingly important. Due to the GPU’s significant power consumption, developing high- performance and power-efficient GPU systems is a critical challenge. DNN applications need to move a large amount of data between memory and the processing cores, which consumes a great amount of NoC power. However, prior proposed lossless data compressions cannot achieve optimal performance and energy efficiency because they did not take advantage of the error resilience of DNNs. In this work, we propose an NoC architecture that can reduce power consumption without compromising performance and accu- racy. Our technique takes advantage of the error resilience of DNNs as well as the data locality in the floating-point data representation of DNNs. Each data packet is reorganized by grouping data with similar bits such as in the exponents, and redundant bits are sent only once. We further compress the mantissa fields by appropri- ately selecting "proxy" values for data sharing the same exponent. Our evaluation results show that the proposed technique can ef- fectively reduce the amount of data transmitted and lead to better performance and power trade-offs while preserving accuracy. 

Genomic analysis is the study of genes which includes the identification, measurement, or comparison of genomic features. Genomics research is of great importance to our society because it can be used to detect diseases, create vaccines, and develop drugs and treatments. As a type of general-purpose accelerators with massive parallel processing capability, GPUs have been recently used for genomics analysis. Developing GPU-based hardware and software frameworks for genome analysis is becoming a promising research area. To support this type of research, benchmarks are needed that can feature representative, concurrent, and diverse applications running on GPUs. In this work, we created a benchmark suite called Genomics-GPU, which contains 10 widely-used genomic analysis applications. It covers genome comparison, matching, and clustering for DNAs and RNAs. We also adapted these applications to exploit the CUDA Dynamic Parallelism (CDP), a recent advanced feature supporting dynamic GPU programming, to further improve the performance. Our benchmark suite can serve as a basis for algorithm optimization and also facilitate GPU architecture development for genomics analysis.