Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, and program comprehension. Despite its importance, performing inter-procedural dataflow analysis on large-scale programs is well-known to be challenging. In this article, we propose a novel distributed analysis framework supporting the general inter-procedural dataflow analysis. Inspired by large-scale graph processing, we devise dedicated distributed worklist algorithms for both whole-program analysis and incremental analysis. We implement these algorithms and develop a distributed framework called BigDataflow running on a large-scale cluster. The experimental results validate the promising performance of BigDataflow—BigDataflow can finish analyzing the program of million lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency.

Spiking neural networks (SNNs) are an alternative computational paradigm to artificial neural networks (ANNs) that have attracted attention due to their event-driven execution mechanisms, enabling extremely low energy consumption. However, a significant challenge and opportunity in SNNs is to optimize memory and compute costs while maintaining accuracy, thereby further reducing energy consumption. Model quantization has been proposed as a promising technique to improve the running efficiency via the number of data bits reduction. Whereas, this technique has yet to be well studied in the neuromorphic computing domain. The underlying reason is that the behaviors of SNNs are quite different from those of ANNs, making 1) the accuracy of SNNs usually sensitive to data precision, and 2) the introduction of a temporal dimension to characterize neuronal dynamics. In this paper, we present NeuronQuant, an accurate and energy-efficient quantization framework to reduce the precision of neurons while maintaining accuracy. The key insight is to design a post-training quantization method guided by the activity of neurons, efficiently reducing the bit-width of parameters based on local relationships within neurons. Additionally, a budget-aware mixed bit-width allocation strategy for the total model size enables the adaptive growth and narrowing of precision in each layer, leading to a mixed-precision quantization scheme of the desired size. Extensive evaluations demonstrate that NeuronQuant can achieve a compressed SNN with 5.2 bits on average and 1.51× power consumption reduction with a superior model accuracy which is quite impressive for SNN. Code is released at https://github.com/shieldforever/NeuronQuant.

Out-of-distribution (OOD) detection remains challenging for deep learning models, particularly when test-time OOD samples differ significantly from training outliers. We propose OODD, a novel test-time OOD detection method that dynamically maintains and updates an OOD dictionary without fine-tuning. Our approach leverages a priority queue-based dictionary that accumulates representative OOD features during testing, combined with an informative inlier sampling strategy for in-distribution (ID) samples. To ensure stable performance during early testing, we propose a dual OOD stabilization mechanism that leverages strategically generated outliers derived from ID data. To our best knowledge, extensive experiments on the OpenOOD benchmark demonstrate that OODD significantly outperforms existing methods, achieving a 26.0% improvement in FPR95 on CIFAR-100 Far OOD detection compared to the state-of-the-art approach. Furthermore, we present an optimized variant of the KNN-based OOD detection framework that achieves a 3x speedup while maintaining detection performance. Our code is available at https://github.com/zxk1212/OODD.

Language models have achieved unprecedented success in natural language processing tasks and have recently been adapted for biological sequences. However, GPUs still encounter significant performance bottlenecks when running BERT-style natural language models. Moreover, due to the vastly greater number of tokens in protein sequences compared to human languages (as illustrated in Figure 1), directly transferring language models from human languages to proteins results in significant changes in runtime behavior. Consequently, optimizations designed for short-input BERT models are less effective for protein language models.In this paper, we propose a novel GPU-based cross-layer optimization strategy. From an architectural perspective, our approach leverages intra- and inter-operator parallelism, multilevel data computation and warp parallelism to fully utilize GPU features. From an algorithmic perspective, we address the issue of varying lengths in protein input sequences and reduce memory overhead through padding removal. Experimental results demonstrate that ProXplore achieves a 1.8× increase in inference speed on the TAPE benchmark without a significant loss in prediction accuracy. This improvement effectively overcomes performance bottlenecks and provides substantial benefits for protein sequence analysis and other bioinformatics applications.

The increasing capacity and reducing cost of the main memory made in-memory data analytics systems widely deployed as they could provide higher throughput and lower latency. Since the data resides in memory, computational throughput becomes a crucial factor in the performance of these systems rather than disk accesses. Single instruction multiple data (SIMD) is an effective mechanism to improve computational performance, which has been well studied to accelerate data analytics systems. However, the state-of-the-art methods focus on using SIMD more efficiently while neglecting scalar execution units.In this paper, we present the hybrid execution framework (HEF) to co-utilize SIMD and scalar execution units for the data analytics workload. We also extend the concept of pack to eliminate the data dependency between adjacent instructions, achieving shorter instruction execution intervals. Experimental results show that the hybrid execution achieves up to 2.38× and 1.45× better performance compared with the purely scalar and SIMD implementation on the star schema benchmark (SSB) queries, respectively. Besides, HEF performs better than the state-of-the-art system Voila for a majority of queries in SSB under all data scales.

Memory safety issues are the intrinsic diseases of C/C++ programs. Dynamic memory safety enforcement as the dominant approach has an advantage in high effectiveness, yet suffers from prohibitively high runtime overhead. Existing attempts to reduce the overhead are either labor-intensive, tightly dependent on specific hardware/compiler support, or poorly effective.

Abstract: Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance, performing interprocedural dataflow analysis on large-scale programs is well known to be challenging.In this paper, we propose a novel distributed analysis framework supporting the general interprocedural dataflow analysis.Inspired by large-scale graph processing, we devise a dedicated distributed worklist algorithm tailored for interprocedural dataflow analysis. We implement the algorithm and develop a distributed framework called BigDataflow running on a large-scale cluster.The experimental results validate the promising performance of BigDataflow – it can finish analyzing the program of millions lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency.