NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings

Near-infrared photoactivatable control of Ca signaling and optogenetic immunomodulation

The application of current channelrhodopsin-based optogenetic tools is limited by the lack of strict ion selectivity and the inability to extend the spectra sensitivity into the near-infrared (NIR) tissue transmissible range. Here we present an NIR-stimulable optogenetic platform (termed Opto-CRAC ) that selectively and remotely controls Ca2+ oscillations and Ca2+-responsive gene expression to regulate the function of non-excitable cells, including T lymphocytes, macrophages and dendritic cells. When coupled to upconversion nanoparticles, the optogenetic operation window is shifted from the visible range to NIR wavelengths to enable wireless photoactivation of Ca2+-dependent signaling and optogenetic modulation of immunoinflammatory responses. In a mouse model of melanoma by using ovalbumin as surrogate tumor antigen, Opto-CRAC has been shown to act as a genetically-encoded photoactivatable adjuvant to improve antigen-specific immune responses to specifically destruct tumor cells. Our study represents a solid step forward towards the goal of achieving remote control of Ca2+-modulated activities with tailored function

Near-infrared photoactivatable control of Ca2+ signaling and optogenetic immunomodulation

The application of current channelrhodopsin-based optogenetic tools is limited by the lack of strict ion selectivity and the inability to extend the spectra sensitivity into the near-infrared (NIR) tissue transmissible range. Here we present an NIR-stimulable optogenetic platform (termed 'Opto-CRAC') that selectively and remotely controls Ca(2+) oscillations and Ca(2+)-responsive gene expression to regulate the function of non-excitable cells, including T lymphocytes, macrophages and dendritic cells. When coupled to upconversion nanoparticles, the optogenetic operation window is shifted from the visible range to NIR wavelengths to enable wireless photoactivation of Ca(2+)-dependent signaling and optogenetic modulation of immunoinflammatory responses. In a mouse model of melanoma by using ovalbumin as surrogate tumor antigen, Opto-CRAC has been shown to act as a genetically-encoded 'photoactivatable adjuvant' to improve antigen-specific immune responses to specifically destruct tumor cells. Our study represents a solid step forward towards the goal of achieving remote and wireless control of Ca(2+)-modulated activities with tailored function. DOI: http://dx.doi.org/10.7554/eLife.10024.00

Extreme Acceleration and Seamless Integration of Raw Data Processing

New sources of big data such as the Internet, mobile applications, data-driven science and large-scale sensors (IoT) are driving demand for growing computing performance. Efficient analysis of data in native raw formats in real-time is increasingly important because of rapid data generation, demand for analytics, and insights for immediate responses. Traditional data processing systems can deliver high-performance on loaded data, but transforming raw data into these loaded formats is expensive. Data transformations, rather than arithmetic operations, dominate the task performance. Such transformation is a critical performance bottleneck of raw data processing. We propose the ACCelerated Operators for Raw Data Analysis (ACCORDA), a combined software and hardware approach, to accelerate data analytics on unloaded raw data. ACCORDA enables real-time decision making and fast knowledge exploration on dirty, diverse, and ad-hoc raw data, such as fresh sensor data, web crawled, and business records. The Unified Transformation Accelerator (UTA) is ACCORDA’s hardware approach. It creates flexible architecture support for data transformation in analytical workloads. Exploiting efficient hardware customization, a scratchpad memory, and MIMD parallelism, Unstructured Data Processor (UDP) is a novel hardware accelerator based on the UTA approach. UDP demonstrates the feasibility of the UTA approach. We propose the UDP’s instruction set, micro-architecture, and compiler toolchain. UDP has four unique features: multi-way dispatch, variable-size symbol, flexible-source dispatch, and flexible addressing. Extensive evaluation of data transformation kernels, ranging from compression to pattern matching, shows UDP achieves 20x average speedup and 1,900x energy efficiency when compared to an 8-thread CPU. The UDP’s implementation is >100x less power and area than a single CPU core. The Accelerated Transformation Operators (ATO) is ACCORDA’s software approach. ATO applies two design choices for integrating data transformation acceleration – sub-typing operator interface with encodings and uniform worker model. The encoding-extended interface enables new accelerated operators to be included in a query plan. Runtime data formats can be transformed to meet the encoding requirements of accelerated operator implementations. In addition, query optimizer can re-order encoding operators for lazy data transformation, and fuse them to improve data locality and reduce transformation cost. Uniform worker model preserves system software architectures and provides a uniform runtime to the execution engine, empowering rule-based optimizers to drive flexible encoding-based optimization. We demonstrate that the key enablers are the UTA’s low-cost, high-performance design and its in memory-hierarchy integration for efficient, low-overhead data sharing with CPUs. Together, they enable flexible software exploitation of hardware acceleration and worker thread integration. ACCORDA achieves significant acceleration on data transformation tasks, with speedups up to 4.9x on regex matching, 2.6x on decompression, 2x on parsing, and 20x on deserialization when compared to an 8-thread CPU. We evaluate ACCORDA using end-to-end TPC-H queries on unloaded data with raw format. Hardware acceleration contributes 1.1x-6.3x improvement alone, and software elements such as query optimization for data encoding unlocked by ATO deliver an additional 1.2x-11.8x speedup. Combining UTA’s acceleration and ATO’s encoding optimization, ACCORDA achieves 3.3x-13.2x overall speedups on single- thread performance when compared to the baseline Spark SQL. We further show that this performance benefit is robust across format complexity of query predicates and selectivity (data statistics). Furthermore, ACCORDA robustly matches or even outperforms (by up to 11.4x) prior systems that depend on caching transformed data, while computing on raw, unloaded data

Natural and semi-natural land dynamics under water resource change from 1990 to 2015 in the Tarim Basin, China

The Tarim Basin is a typical arid area and has the world’s most severe desertification of natural and semi-natural land due to limited water resources. However, knowledge about the impacts of changes in water resources on the spatio-temporal dynamics of natural and semi-natural land is still limited. We analyzed the spatio-temporal changes in natural and semi-natural land and the associations with desertification in the Tarim Basin during the period 1990–2015. We then investigated the changes in water resources and the consequent impacts on the spatio-temporal changes of natural and semi-natural land by integrating Gravity Recovery and Climate Experiment territorial water storage data and field observations. The results showed that a total area of 10.32 × 10 ^3 km ^2 of natural and semi-natural land was converted to desert during the period 1990–2015. Desert vegetation type and saline type were the natural and semi-natural land types most sensitive to conversion to desert. The area of natural and semi-natural land decreased by 0.83% every year, and the proportion of desertified land was 34.79% on average during the period 2000–2010; this is less than for the period 1990–2000 (1.14% yr ^−1 and 52.01%) due to increased availability of water resources from the water conveyance program. However, the rate of decrease of natural and semi-natural land area (0.93% yr ^−1 ) and the proportion of desertified land (58.88%) rose again during the period 2010–2015 due to the rapid decrease in water resources. During the period 2000–2015, the rate of loss of natural and semi-natural land area (7.89%) in the region with decreased water resources was about twice that in the region with increased water resources (3.88%), highlighting the critical role of water resources in maintaining natural and semi-natural land and slowing desertification

Tracking the spatio-temporal change of cropping intensity in China during 2000–2015

Improvement in the efficiency of farmland utilization and multiple cropping systems are of prime importance for achieving food security in China. Therefore, spatially-explicit analysis detecting trends of cropping intensity are important preconditions for sustainable agricultural development. However, knowledge about the spatiotemporal dynamics of cropping intensity in China remains limited. In this study, we generated annual cropping intensity maps in China during 2000–2015 using a rule-based algorithm and MOD09A1 time series imagery. We then analyzed the spatio-temporal changes of cropping intensity. The results showed single-cropping and double-cropping areas were about 1.28 ± 0.027 × 10 ^6 km ^2 and 0.52 ± 0.027 × 10 ^6 km ^2 in China in 2015 and their areas were relatively stable from 2000–2015. However, cropping intensity had substantial spatial changes during 2000–2015. About 0.164 ± 0.026 × 10 ^6 km ^2 of single-cropping area was converted to double-cropping area, which mainly occurred in the Huang-Huai-Hai Region. About 0.193 ± 0.028 × 10 ^6 km ^2 of double-cropping area was converted to single-cropping area, which mainly occurred in the southern part of China. About 85% of croplands with decreases in cropping intensity were located in the southern part of China, and about 80% of croplands with increases in cropping intensity was distributed in the Huang-Huai-Hai Region and the northern part of the Middle and Lower Reaches of the Yangtze River region ( p  < 0.05). The landscapes of different cropping systems tended to be homogenized in major agricultural production regions