CampProf: A Visual Performance Analysis Tool for Memory Bound GPU Kernels

Current GPU tools and performance models provide some common architectural insights that guide the programmers to write optimal code. We challenge these performance models, by modeling and analyzing a lesser known, but very severe performance pitfall, called 'Partition Camping', in NVIDIA GPUs. Partition Camping is caused by memory accesses that are skewed towards a subset of the available memory partitions, which may degrade the performance of memory-bound CUDA kernels by up to seven-times. No existing tool can detect the partition camping effect in CUDA kernels. We complement the existing tools by developing 'CampProf', a spreadsheet based, visual analysis tool, that detects the degree to which any memory-bound kernel suffers from partition camping. In addition, CampProf also predicts the kernel's performance at all execution configurations, if its performance parameters are known at any one of them. To demonstrate the utility of CampProf, we analyze three different applications using our tool, and demonstrate how it can be used to discover partition camping. We also demonstrate how CampProf can be used to monitor the performance improvements in the kernels, as the partition camping effect is being removed. The performance model that drives CampProf was developed by applying multiple linear regression techniques over a set of specific micro-benchmarks that simulated the partition camping behavior. Our results show that the geometric mean of errors in our prediction model is within 12% of the actual execution times. In summary, CampProf is a new, accurate, and easy-to-use tool that can be used in conjunction with the existing tools to analyze and improve the overall performance of memory-bound CUDA kernels

Architecture-Aware Optimization on a 1600-core Graphics Processor

The graphics processing unit (GPU) continues to make significant strides as an accelerator in commodity cluster computing for high-performance computing (HPC). For example, three of the top five fastest supercomputers in the world, as ranked by the TOP500, employ GPUs as accelerators. Despite this increasing interest in GPUs, however, optimizing the performance of a GPU-accelerated compute node requires deep technical knowledge of the underlying architecture. Although significant literature exists on how to optimize GPU performance on the more mature NVIDIA CUDA architecture, the converse is true for OpenCL on the AMD GPU. Consequently, we present and evaluate architecture-aware optimizations for the AMD GPU. The most prominent optimizations include (i) explicit use of registers, (ii) use of vector types, (iii) removal of branches, and (iv) use of image memory for global data. We demonstrate the efficacy of our AMD GPU optimizations by applying each optimization in isolation as well as in concert to a large-scale, molecular modeling application called GEM. Via these AMD-specific GPU optimizations, the AMD Radeon HD 5870 GPU delivers 65% better performance than with the wellknown NVIDIA-specific optimizations

Efficient Sparse Matrix-Vector Multiplication on GPUs Using the CSR Storage Format.

Abstract-The performance of sparse matrix vector multiplication (SpMV) is important to computational scientists. Compressed sparse row (CSR) is the most frequently used format to store sparse matrices. However, CSR-based SpMV on graphics processing units (GPUs) has poor performance due to irregular memory access patterns, load imbalance, and reduced parallelism. This has led researchers to propose new storage formats. Unfortunately, dynamically transforming CSR into these formats has significant runtime and storage overheads. We propose a novel algorithm, CSR-Adaptive, which keeps the CSR format intact and maps well to GPUs. Our implementation addresses the aforementioned challenges by (i) efficiently accessing DRAM by streaming data into the local scratchpad memory and (ii) dynamically assigning different numbers of rows to each parallel GPU compute unit. CSR-Adaptive achieves an average speedup of 14.7× over existing CSR-based algorithms and 2.3× over clSpMV cocktail, which uses an assortment of matrix formats

Architecture-Aware Mapping and Optimization on a 1600-Core GPU

Abstract—The graphics processing unit (GPU) continues to make in-roads as a computational accelerator for highperformance computing (HPC). However, despite its increasing popularity, mapping and optimizing GPU code remains a difficult task; it is a multi-dimensional problem that requires deep technical knowledge of GPU architecture. Although substantial literature exists on how to map and optimize GPU performance on the more mature NVIDIA CUDA architecture, the converse is true for OpenCL on an AMD GPU, such as the 1600-core AMD Radeon HD 5870 GPU. Consequently, we present and evaluate architecture-aware mapping and optimizations for the AMD GPU. The most prominent of which include (i) explicit use of registers, (ii) use of vector types, (iii) removal of branches, and (iv) use of image memory for global data. We demonstrate the efficacy of our AMD GPU mapping and optimizations by applying each in isolation as well as in concert to a large-scale, molecular modeling application called GEM. Via these AMD-specific GPU optimizations, our optimized OpenCL implementation on an AMD Radeon HD 5870 delivers more than a fourfold improvement in performance over the basic OpenCL implementation. In addition, it outperforms our optimized CUDA version on an NVIDIA GTX280 by 12%. Overall, we achieve a speedup of 371-fold over a serial but hand-tuned SSE version of our molecular modeling application, and in turn, a 46-fold speedup over an ideal scaling on an 8-core CPU. Keywords-GPU; AMD; OpenCL; NVIDIA; CUDA; performance evaluation; I