data management | UCSC OSPO

Optimizing FasTensor: Enabling Efficient Tensor Execution on GPUs

Mon, 05 Jun 2023 00:00:00 +0000

Greetings,

I am Rishabh Singh, and I am excited to be part of the 2023 Google Summer of code program. My proposal under the mentorship of John Wu and Bin Dong focuses on optimizing the FasTensor tensor computing library for efficient usage on GPUs, specifically targeting tensor contraction while preserving structure-locality. This optimization is crucial for scientific applications and advanced AI model training. Throughout the project, I will develop custom computational operations for GPUs, implement FasTensor on GPUs, assess its performance, and provide comprehensive documentation. By the end, I aim to deliver a working implementation, a performance report, and a detailed execution mechanism guide. Leveraging my background in software engineering and machine learning, I will utilize languages like C++ and OpenMP to ensure efficient memory management and data movement. Stay tuned for regular updates and informative blogs as I progress through the summer.

Proactive Data Containers (PDC)

Sun, 12 Feb 2023 00:00:00 +0000

Proactive Data Containers (PDC) are containers within a locus of storage (memory, NVRAM, disk, etc.) that store science data in an object-oriented manner. Managing data as objects enables powerful optimization opportunities for data movement and transformations, and storage mechanisms that take advantage of the deep storage hierarchy and enable automated performance tuning.

Command line and python interface to an object-centric data management system

Topics: Python, object-centric data management, PDC
Skills: Linux, C, Python
Difficulty: Medium
Size: Large (350 hours)
Mentors: Houjun Tang, Suren Byna

Proactive Data Containers (PDC) is an object-centric data management system for scientific data on high performance computing systems. It manages objects and their associated metadata within a locus of storage (memory, NVRAM, disk, etc.). Managing data as objects enables powerful optimization opportunities for data movement and transformations, and storage mechanisms that take advantage of the deep storage hierarchy and enable automated performance tuning. This project includes developing and updating efficient and user friendly command line and Python interfaces for PDC.

FasTensor

Mon, 07 Nov 2022 10:15:56 -0700

FasTensor is a parallel execution engine for user-defined functions on multidimensional arrays. The user-defined functions follow the stencil metaphor used for scientific computing and is effective for expressing a wide range of computations for data analyses, including common aggregation operations from database management systems and advanced machine learning pipelines. FasTensor execution engine exploits the structural-locality in the multidimensional arrays to automate data management operations such as file I/O, data partitioning, communication, parallel execution, and so on.

Continuous Integration

Topics: Data Management, Analytics
Skills: C++, github
Difficulty: Medium
Size: Large (350 hours)
Mentor: John Wu, Bin Dong, Suren Byna

Develop a test suite for the public API of FasTensor
Automate execution of the test suite
Document the continuous integration process
Develop performance testing suite

FasTensor

Mon, 07 Nov 2022 10:15:56 -0700

Tensor execution engine on GPU

Topics: Data Management, Analytics
Skills: C++, github
Difficulty: Difficult
Size: Large (350 hours)
Mentor: John Wu, Bin Dong, Suren Byna

Tensor based computing is needed by scientific applications and now advanced AI model training. Most tensor libraries are hand customized and optimized on GPU, and most of they only serve one kind of application. For example, TensorFlow is only optimized for AI model training. Optimizing generic tensor computing libraries on GPU can benefit wide applications. Our FasTensor, as a generic tensor computing library, can only work efficiently on CPU now. How to run the FasTensor on GPU is still none-explored work. Research and development challenges will include but not limited to: 1) how to maintain structure-locality of tensor data on GPU; 2) how to reduce the performance loss when the structure-locality of tensor is broken on GPU.

Develop a mechanism to move user-define computing kernels onto GPU
Evaluate the performance of the execution engine
Document the execution mechanism
Develop performance testing suite

Continuous Integration

Topics: Data Management, Analytics
Skills: C++, github
Difficulty: Medium
Size: Large (300 hours)
Mentor: John Wu, Bin Dong, Suren Byna

Develop a test suite for the public API of FasTensor
Automate execution of the test suite
Document the continuous integration process

Proactive Data Containers (PDC)

Mon, 07 Nov 2022 10:15:56 -0700

Python interface to an object-centric data management system

Topics: Python, object-centric data management, PDC
Skills: Python, C, PDC
Difficulty: Medium
Size: Large (350 hours)
Mentor: Suren Byna, Houjun Tang

Proactive Data Containers (PDC) is an object-centric data management system for scientific data on high performance computing systems. It manages objects and their associated metadata within a locus of storage (memory, NVRAM, disk, etc.). Managing data as objects enables powerful optimization opportunities for data movement and transformations, and storage mechanisms that take advantage of the deep storage hierarchy and enable automated performance tuning. Currently PDC has a C interface. Providing a python interface would make it easier for more Python applications to utilize it.