Feng Zhang
Latest
- A comprehensive taxonomy of prompt engineering techniques for large language models
- ComStar: Compression-Aware Stream Query for Heterogeneous Hybrid Architecture
- Enabling Tile-Based Direct Query on Adaptively Compressed Data With GPU Acceleration
- Breaking the Edge: Enabling Efficient Neural Network Inference on Integrated Edge Devices
- HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search
- HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search
- PluS: Highly Efficient and Expandable ML Compiler with Pluggable Graph Schedules
- TraceFlow: Efficient Trace Analysis for Large-Scale Parallel Applications via Interaction Pattern-Aware Trace Distribution
- Compressed Data Direct Computing for Databases
- F-TADOC: FPGA-Based Text Analytics Directly on Compression with HLS
- G-Learned Index: Enabling Efficient Learned Index on GPU
- Graph-Centric Performance Analysis for Large-Scale Parallel Applications
- BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach
- CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression
- Enabling Efficient Random Access to Hierarchically Compressed Text Data on Diverse GPU Platforms
- Optimizing DNNs With Partially Equivalent Transformations and Automated Corrections
- CompressDB: Enabling Efficient Compressed Data Direct Processing for Various Databases
- Detecting Performance Variance for Parallel Applications Without Source Code
- Exploring Data Analytics Without Decompression on Embedded GPU Systems
- Exploring Query Processing on CPU-GPU Integrated Edge Device
- Leveraging Code Snippets to Detect Variations in the Performance of HPC Systems
- Payment behavior prediction on shared parking lots with TR-GCN
- Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction
- POCLib: A High-Performance Framework for Enabling Near Orthogonal Processing on Compression
- An Efficient Parallel Secure Machine Learning Framework on GPUs
- Automatic Irregularity-Aware Fine-Grained Workload Partitioning on Integrated Architectures
- G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression
- G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression
- Preface
- TADOC: Text analytics directly on compression
- Enabling Efficient Random Access to Hierarchically-Compressed Data
- ParSecureML: An Efficient Parallel Secure Machine Learning Framework on GPUs
- Payment Behavior Prediction and Statistical Analysis for Shared Parking Lots
- PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction
- TADOC: Text Analytics Directly on Compression
- Guest Editorial: Special Issue on Network and Parallel Computing for Emerging Architectures and Applications
- Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors
- Statistical Analysis and Prediction of Parking Behavior
- An adaptive breadth-first search algorithm on integrated architectures
- Efficient Document Analytics on Compressed Data: Method, Challenges, Algorithms, Insights
- Network and Parallel Computing - 15th IFIP WG 10.3 International Conference, NPC 2018, Muroran, Japan, November 29 - December 1, 2018, Proceedings
- Zwift: A Programming Framework for High Performance Text Analytics on Compressed Data
- FinePar: irregularity-aware fine-grained workload partitioning on integrated architectures
- Understanding Co-Running Behaviors on Integrated CPU/GPU Architectures
- Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays
- To Co-run, or Not to Co-run: A Performance Study on Integrated Architectures