General-purpose GPUs have been widely utilized to accelerate parallel applications. Given a relatively complex programming model and fast architecture evolution, producing efficient GPU code is nontrivial. A variety of simulation and profiling tools have been developed to aid GPU application optimization and architecture design. However, existing tools are either limited by insufficient insights or lacking in support across different GPU architectures, runtime and driver versions. This paper presents CUDAAdvisor, a profiling framework to guide code optimization in modern NVIDIA GPUs. CUDAAdvisor performs various fine-grained analyses based on the profiling results from GPU kernels, such as memory-level analysis (e.g., reuse distance and memory divergence), control flow analysis (e.g., branch divergence) and code-/data-centric debugging. Unlike prior tools, CUDAAdvisor supports GPU profiling across different CUDA versions and architectures, including CUDA 8.0 and Pascal architecture. We demonstrate several case studies that derive significant insights to guide GPU code optimization for performance improvement.

Heterogeneous memory (HM, also known as hybrid memory) has become popular in emerging parallel architectures due to its programming flexibility and energy efficiency. Unlike the traditional memory subsystem, HM consists of fast and slow components. Usually, the fast memory lacks hardware support, which puts extra burdens on programmers and compilers for explicit data placement. Thus, HM provides both opportunities and challenges with programming parallel codes. It is important to understand how to utilize HM and set expectations on the benefits of HM. Prior work principally uses simulators to study HM, which lacks the analysis on a real hardware. To address this issue, this paper experiments with a real system—the TI KeyStone II—to study HM. We make three contributions. First, we develop a set of parallel benchmarks to characterize the performance and power efficiency of HM. It is the first benchmark suite with OpenMP 4.0 features that is functional on real HM architectures. Second, we build a profiling tool to provide guidance for placing data in HM. Our tool analyzes memory access patterns and provides high-level feedback at the source-code level for optimization. Third, we apply the data placement optimization to our benchmarks and evaluate the effectiveness of HM in boosting performance and saving energy.

Application profiling is an important performance analysis technique, when an application under test is analyzed dynamically to determine its space and time complexities and the usage of its instructions. A big and important challenge is to profile nontrivial web applications with large numbers of combinations of their input parameter values. Identifying and understanding particular subsets of inputs leading to performance bottlenecks is mostly manual, intellectually intensive and laborious procedure. We propose a novel approach for automating performance bottleneck detection using search-based input-sensitive application profiling. Our key idea is to use a genetic algorithm as a search heuristic for obtaining combinations of input parameter values that maximizes a fitness function that represents the elapsed execution time of the application. We implemented our approach, coined as Genetic Algorithm-driven Profiler (GA-Prof) that combines a search-based heuristic with contrast data mining of execution traces to accurately determine performance bottlenecks. We evaluated GA-Prof to determine how effectively and efficiently it can detect injected performance bottlenecks into three popular open source web applications. Our results demonstrate that GA-Prof efficiently explores a large space of input value combinations while automatically and accurately detecting performance bottlenecks, thus suggesting that it is effective for automatic profiling.