Yang Li
ABSTRACT
Data-intensive applications are becoming increasingly popular. However, only a few of them with high volume can afford dedicated hardware acceleration (such as Neural Network Processor, or NPU) or platform-specific software implementation (such as Tensorflow running on GPU). In this paper, we propose a hardware and software transparent framework for the acceleration of general-purpose data-intensive applications. Our framework is based on a key insight that most data-intensive applications spend the vast majority of their execution time on some inner loops with abundant opportunities for Data-Level Parallelism (DLP). In particular, we propose SALAD, a static analyzer for loop acceleration by exploiting DLP in hot loops under the LLVM (LLVM compiler infrastructure) framework. In contrast to traditional DLP exploration techniques, SALAD is both software and architectural transparent, without the need to change either the source code or binary code, and does not need vectorized instruction set architecture (ISA) extensions. Instead, it directly works on the program binary code and generates a profile for DLP opportunities in the binary. This profile will be fed to the hardware accelerator transparently to speed up execution. With the experiments result, we estimate that the DLP information provided by SALAD could result in 3.6x-60.2x speedups on a set of benchmarks, depending on their inherent DLP.
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