ACM Transactions on Architecture and Code Optimization最新文献

Reducing energy consumption while providing performance and quality guarantees is crucial for computing systems ranging from battery-powered embedded systems to data centers. This article considers approximate iterative applications executing on heterogeneous multi-core platforms under user-specified performance and quality targets. We note that allowing a slight yet bounded relaxation in solution quality can considerably reduce the required iteration count and thereby can save significant amounts of energy. To this end, this article proposes Approx-RM, a resource management scheme that reduces energy expenditure while guaranteeing a specified performance as well as accuracy target. Approx-RM predicts the number of iterations required to meet the relaxed accuracy target at runtime. The time saved generates execution-time slack, which allows Approx-RM to allocate fewer resources on a heterogeneous multi-core platform in terms of DVFS, core type, and core count to save energy while meeting the performance target. Approx-RM contributes with lightweight methods for predicting the iteration count needed to meet the accuracy target and the resources needed to meet the performance target. Approx-RM uses the aforementioned predictions to allocate just enough resources to comply with quality of service constraints to save energy. Our evaluation shows energy savings of 31.6%, on average, compared to Race-to-idle when the accuracy is only relaxed by 1%. Approx-RM incurs timing and energy overheads of less than 0.1%.

Fast Fourier transform (FFT) is widely used in computing applications in large-scale parallel programs, and data communication is the main performance bottleneck of FFT and seriously affects its parallel efficiency. To tackle this problem, we propose a new large-scale FFT framework, MFFT, which optimizes parallel FFT with a new mixed-precision optimization technique, adopting the “high precision computation, low precision communication” strategy. To enable “low precision communication”, we propose a shared-exponent floating-point number compression technique, which reduces the volume of data communication, while maintaining higher accuracy. In addition, we apply a two-phase normalization technique to further reduce the round-off error. Based on the mixed-precision MFFT framework, we apply several optimization techniques to improve the performance, such as streaming of GPU kernels, MPI message combination, kernel optimization, and memory optimization. We evaluate MFFT on a system with 4,096 GPUs. The results show that shared-exponent MFFT is 1.23 × faster than that of double-precision MFFT on average, and double-precision MFFT achieves performance 3.53× and 9.48× on average higher than open source library 2Decomp&FFT (CPU-based version) and heFFTe (AMD GPU-based version), respectively. The parallel efficiency of double-precision MFFT increased from 53.2% to 78.1% compared with 2Decomp&FFT, and shared-exponent MFFT further increases the parallel efficiency to 83.8%.

The convolutional neural network (CNN) is an important deep learning method, which is widely used in many fields. However, it is very time consuming to implement the CNN where convolution usually takes most of the time. There are many zero values in feature maps and filters, which leads to redundant calculations and memory accesses if dense methods are used to compute convolution. Many works recently have made use of sparsity to skip the calculations for zero values to reduce the inference time of the CNN. On the graphics processing unit platform, current works cannot fully exploit the sparsity of the feature map and achieve satisfactory performance. Therefore, we design a new parallel strategy to transform the feature map into a new storage format to avoid the redundant computation of zero values on graphics processing units. Also considering the sparsity in the feature map, we propose a fused storage format to combine the convolution operation with the following pooling operation, to further improve the performance. We carry out experiments with mainstream CNN models and achieve better performance compared with cuDNN and cuSPARSE. For VGG-19, ResNet-50, DenseNet-121, and RegNetX-16GF, 1.97×, 2.23×, 2.74×, and 1.58× speedups respectively are obtained over cuDNN. The speedups over cuSPARSE respectively are 2.10×, 1.83×, 2.35×, and 1.35× when only using the first method.

GPU database systems are an effective solution to query optimization, particularly with compilation and data caching. They fall short, however, in end-to-end workloads, as existing compiler toolchains are too expensive for use with short-running queries. In this work, we define and evaluate a runtime-suitable query compilation pipeline for NVIDIA GPUs that extracts high performance with only minimal optimization. In particular, our balanced approach successfully trades minor slowdowns in execution for major speedups in compilation, even as data sizes increase. We demonstrate performance benefits compared to both CPU and GPU database systems using interpreters and compilers, extending query compilation for GPUs beyond cached use cases.