44 research outputs found
swTVM: Exploring the Automated Compilation for Deep Learning on Sunway Architecture
The flourish of deep learning frameworks and hardware platforms has been
demanding an efficient compiler that can shield the diversity in both software
and hardware in order to provide application portability. Among the exiting
deep learning compilers, TVM is well known for its efficiency in code
generation and optimization across diverse hardware devices. In the meanwhile,
the Sunway many-core processor renders itself as a competitive candidate for
its attractive computational power in both scientific and deep learning
applications. This paper combines the trends in these two directions.
Specifically, we propose swTVM that extends the original TVM to support
ahead-of-time compilation for architecture requiring cross-compilation such as
Sunway. In addition, we leverage the architecture features during the
compilation such as core group for massive parallelism, DMA for high bandwidth
memory transfer and local device memory for data locality, in order to generate
efficient code for deep learning application on Sunway. The experimental
results show the ability of swTVM to automatically generate code for various
deep neural network models on Sunway. The performance of automatically
generated code for AlexNet and VGG-19 by swTVM achieves 6.71x and 2.45x speedup
on average than hand-optimized OpenACC implementations on convolution and fully
connected layers respectively. This work is the first attempt from the compiler
perspective to bridge the gap of deep learning and high performance
architecture particularly with productivity and efficiency in mind. We would
like to open source the implementation so that more people can embrace the
power of deep learning compiler and Sunway many-core processor
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
The increasing volume of log data produced by software-intensive systems
makes it impractical to analyze them manually. Many deep learning-based methods
have been proposed for log-based anomaly detection. These methods face several
challenges such as high-dimensional and noisy log data, class imbalance,
generalization, and model interpretability. Recently, ChatGPT has shown
promising results in various domains. However, there is still a lack of study
on the application of ChatGPT for log-based anomaly detection. In this work, we
proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By
leveraging the ChatGPT's language interpretation capabilities, LogGPT aims to
explore the transferability of knowledge from large-scale corpora to log-based
anomaly detection. We conduct experiments to evaluate the performance of LogGPT
and compare it with three deep learning-based methods on BGL and Spirit
datasets. LogGPT shows promising results and has good interpretability. This
study provides preliminary insights into prompt-based models, such as ChatGPT,
for the log-based anomaly detection task
Intelligent-Unrolling: Exploiting Regular Patterns in Irregular Applications
Modern optimizing compilers are able to exploit memory access or computation
patterns to generate vectorization codes. However, such patterns in irregular
applications are unknown until runtime due to the input dependence. Thus,
either compiler's static optimization or profile-guided optimization based on
specific inputs cannot predict the patterns for any common input, which leads
to suboptimal code generation. To address this challenge, we develop
Intelligent-Unroll, a framework to automatically optimize irregular
applications with vectorization. Intelligent-Unroll allows the users to depict
the computation task using \textit{code seed} with the memory access and
computation patterns represented in \textit{feature table} and
\textit{information-code tree}, and generates highly efficient codes.
Furthermore, Intelligent-Unroll employs several novel optimization techniques
to optimize reduction operations and gather/scatter instructions. We evaluate
Intelligent-Unroll with sparse matrix-vector multiplication (SpMV) and graph
applications. Experimental results show that Intelligent-Unroll is able to
generate more efficient vectorization codes compared to the state-of-the-art
implementations
Observation of spin-orbit magnetoresistance in metallic thin films on magnetic insulators
A magnetoresistance effect induced by the Rashba spin-orbit interaction was
predicted, but not yet observed, in bilayers consisting of normal metal and
ferromagnetic insulator. Here, we present an experimental observation of this
new type of spin-orbit magnetoresistance (SOMR) effect in a bilayer structure
Cu[Pt]/Y3Fe5O12 (YIG), where the Cu/YIG interface is decorated with nanosize Pt
islands. This new MR is apparently not caused by the bulk spin-orbit
interaction because of the negligible spin-orbit interaction in Cu and the
discontinuity of the Pt islands. This SOMR disappears when the Pt islands are
absent or located away from the Cu/YIG interface, therefore we can
unambiguously ascribe it to the Rashba spin-orbit interaction at the interface
enhanced by the Pt decoration. The numerical Boltzmann simulations are
consistent with the experimental SOMR results in the angular dependence of
magnetic field and the Cu thickness dependence. Our finding demonstrates the
realization of the spin manipulation by interface engineering.Comment: 12 pages, 4 figures, 14 pages in supplementary. To appear on Science
Advance
DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery
To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000