Makrani et al., 2018 - Google Patents
Compressive sensing on storage data: An effective solution to alleviate i/0 bottleneck in data-intensive workloadsMakrani et al., 2018
View PDF- Document ID
- 991367618104614124
- Author
- Makrani H
- Sayadi H
- Manoj S
- Raftirad S
- Homayoun H
- Publication year
- Publication venue
- 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)
External Links
Snippet
The gap between computation speed and I/O access on modern computing systems imposes processing limitations in data-intensive applications. Employing high-end memory has proven not to enhance the performance for I/O bound applications, given the low …
- 238000003860 storage 0 title abstract description 28
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F12/00—Accessing, addressing or allocating within memory systems or architectures
- G06F12/02—Addressing or allocation; Relocation
- G06F12/08—Addressing or allocation; Relocation in hierarchically structured memory systems, e.g. virtual memory systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
-
- H—ELECTRICITY
- H03—BASIC ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same information or similar information or a subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sadi et al. | Efficient spmv operation for large and highly sparse matrices using scalable multi-way merge parallelization | |
| US8115659B2 (en) | Method and apparatus for efficient gathering of information in a multicore system | |
| Makrani et al. | Compressive sensing on storage data: An effective solution to alleviate i/0 bottleneck in data-intensive workloads | |
| US20190370645A1 (en) | Deep neural network accelerator with fine-grained parallelism discovery | |
| US20200145680A1 (en) | Parallel coding of syntax elements for jpeg accelerator | |
| Zou et al. | FlexAnalytics: a flexible data analytics framework for big data applications with I/O performance improvement | |
| Bicer et al. | Integrating online compression to accelerate large-scale data analytics applications | |
| Zeng et al. | An empirical evaluation of columnar storage formats | |
| US8847798B2 (en) | Systems and methods for data compression and parallel, pipelined decompression | |
| JPWO2020190808A5 (en) | ||
| US20210256357A1 (en) | Embedded stochastic-computing accelerator architecture and method for convolutional neural networks | |
| US10848775B2 (en) | Memory layout for JPEG accelerator | |
| CN112256623B (en) | Heterogeneous system-based processing performance optimization method and device | |
| Salamat et al. | NASCENT2: Generic near-storage sort accelerator for data analytics on SmartSSD | |
| Niu et al. | Reuse kernels or activations? A flexible dataflow for low-latency spectral CNN acceleration | |
| Sun et al. | gLSM: Using GPGPU to Accelerate Compactions in LSM-tree-based Key-value Stores | |
| Karakasis et al. | A comparative study of blocking storage methods for sparse matrices on multicore architectures | |
| Finnerty et al. | Dr. BFS: Data centric breadth-first search on FPGAs | |
| Parravicini et al. | Scaling up hbm efficiency of top-k spmv for approximate embedding similarity on fpgas | |
| Sayadi et al. | CUDA memory optimisation strategies for motion estimation | |
| Yuan et al. | Parallel implementation of lossy data compression for temporal data sets | |
| Istvan et al. | Active pages 20 years later: Active storage for the cloud | |
| US11748255B1 (en) | Method for searching free blocks in bitmap data, and related components | |
| Wunderlich et al. | Network coding parallelization based on matrix operations for multicore architectures | |
| Li et al. | An FPGA‐based JPEG preprocessing accelerator for image classification |