The scalable linear algebra package (ScaLAPACK) is a critical library for parallel computing on distributed-memory systems, enabling the development of numerous scientific applications that depend on robust linear algebra operations. However, for specific computations such as the Cholesky decomposition, the native ScaLAPACK routines are not communication-optimal and fail to fully leverage the capabilities of modern parallel architectures. This paper proposes the Parallel Cholesky Factorization (PCF) optimization algorithm designed to address these limitations within the ScaLAPACK framework. The PCF algorithm enhances performance and load balancing by strategically differentiating data partitions across processes. It involves a temporary redistribution of computational workloads to a root process, which performs a concentrated calculation before redistributing the results. This approach ensures a more balanced utilization of CPU resources. Experimental evaluation is conducted on both Intel and Kunpeng processor platforms. The first set of experiments demonstrates that the PCF algorithm achieves an average performance improvement of 30
Fully homomorphic encryption (FHE) enables computation over encrypted data without decryption, ensuring data confidentiality throughout the processing pipeline. However, the complexity and heterogeneity of FHE schemes like CKKS, BFV, and TFHE pose challenges for unified hardware design. In this paper, we propose the Homomorphic Processing Unit (HPU), a general-purpose platform supporting multiple FHE schemes. Unlike fixed-function accelerators, HPU is implemented as a RISC-V extension and introduces a dedicated homomorphic instruction set architecture (H-ISA), comprising micro-instructions for minimal compute kernels and macro-instructions for integration with general-purpose models. Core operations like the number-theoretic transform (NTT) and automorphism are abstracted into a unified instruction layer, mapped onto specialized homomorphic compute units. HPU integrates a collaborative computation and storage design, utilizing a polynomial-level instruction set and a polynomial-granular memory management unit (MMU) to optimize memory and reduce data movement. By combining a complete ISA with a general-purpose computing core, HPU can achieve efficient computing, control, and scheduling. We evaluate the HPU prototype using four multi-FHE benchmarks on field-programmable gate array (FPGA) and 7 nm ASIC. The key results are as follows. 1) HPU achieves up to 1596x and 1174x speedup over CPU for CKKS-like and TFHE operations, respectively. 2) Compared with SOTA FPGA solutions for CKKS and TFHE, HPU achieves 1.36x and 1.2x improvement, respectively. 3) HPU’s ASIC implementation achieves 1.36x speedup over state-of-the-art TFHE accelerators for long short-term memory (LSTM).
With the rapid rise of domestic accelerators and the growing demand for efficient cryptographic algorithm implementations, optimizing the performance of domestic commercial cryptography on homegrown hardware platforms has become a critical research challenge. This paper focuses on utilizing the Chinese Hygon Deeplearning Computing Unit (DCU) as an acceleration platform and proposes a high-performance SM4 encryption implementation. First, we design a collaborative CPU/DCU computing framework, where the CPU handles task scheduling while the DCU performs parallel execution of computationally intensive tasks. Additionally, we leverage the data copy engine of the DCU and employ HIP (heterogeneous-computing interface for portability) stream-based data transmission to enhance computational efficiency. Second, we propose an optimized hierarchical memory access strategy by merging multiple sets of computational data for unified transfer and employing a hierarchical storage scheme based on data characteristics to improve memory access efficiency. Furthermore, we employ GCN (Graphics Core Next) ISA (Instruction Set Architecture) inline assembly to achieve constant-time computation, which enhances both security and computational performance. Finally, we support both single-key and multi-key encryption modes, optimizing memory access and parallel computation for each. The experimental results show that our optimized SM4 implementation HI-SM4 achieves a throughput of 161.81 Gbps in the multikey encryption mode and up to 730.88 Gbps in the single-key encryption mode. Compared with the best existing SM4 implementations on CPU, FPGA, and GPU platforms, our approach achieves performance improvements of 28.22x, 4.50x, and 1.36x, respectively. Our research presents a feasible framework for efficiently implementing indigenous cryptographic algorithms on domestically developed hardware accelerators, thereby contributing to the advancement of high-performance cryptographic computation.
Metaproteomics, utilizing high-throughput liquid chromatography-mass spectrometry (LC-MS), offers a profound understanding of microbial communities. Quantitative metaproteomics further enriches this understanding by measuring relative protein abundance and revealing dynamic changes under different conditions. However, the challenge of missing peptide quantification persists in metaproteomics analysis, particularly in data-dependent acquisition (DDA) mode, where high-intensity precursors for MS2 scans are selected. To tackle this issue, the match-between-runs (MBR) technique is used to transfer peptides between LC-MS runs. Inspired by the benefits of MBR and the need for streamlined metaproteomics data analysis, we develop SEMQuant, an end-to-end software integrating Sipros-Ensemble’s robust peptide identifications with IonQuant’s MBR function. The experiments show that SEMQuant consistently obtains the highest or second highest number of quantified proteins, with notable precision and accuracy. This demonstrates SEMQuant’s effectiveness in conducting comprehensive and accurate quantitative metaproteomics analyses across diverse datasets and highlights its potential to propel advancements in microbial community studies. SEMQuant is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/SEMQuant .
