Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation

Alapfogalmak

The author introduces the ALTO format as a novel approach to efficiently represent and process sparse tensors, enabling parallel execution and reducing memory overhead. By partitioning the multi-dimensional space into balanced line segments, ALTO ensures workload balance and efficient parallel performance.

Kivonat

The content discusses the challenges of processing high-dimensional sparse data using tensor decomposition methods. It introduces the ALTO format as a mode-agnostic representation that improves data locality and enables efficient parallel execution. The ALTO format is compared to traditional sparse tensor storage formats like COO, HiCOO, and CSF, highlighting its benefits in reducing memory usage and improving performance.

The study presents algorithms for Canonical Polyadic Decomposition (CPD) using ALTO, showcasing significant speedups over existing approaches. It also addresses conflict resolution in parallel tensor computations by adapting traversal strategies based on data reuse. Overall, the content emphasizes the importance of efficient processing of sparse tensors for various applications.

Key points:

• Introduction to high-dimensional sparse data challenges.
• Description of ALTO format for efficient tensor representation.
• Comparison with traditional sparse tensor storage formats.
• Algorithms for CPD using ALTO and conflict resolution strategies.
• Emphasis on efficient processing of sparse tensors for improved performance.

Statisztikák

"ALTO achieves more than an order-of-magnitude speedup over the best mode-agnostic formats."
"ALTO achieves more than 5.1× geometric mean speedup at a fraction (25%) of their storage."

Idézetek

"ALTO constructs one tensor copy that is agnostic to both the mode orientation and the irregular distribution of nonzero elements."
"ALTO outperforms the state-of-the-art approaches, achieving more than an order-of-magnitude speedup over the best mode-agnostic formats."

Főbb Kivonatok

Accelerating Sparse Tensor Decomposition Using Adaptive Linearized Representation

by Jan Laukeman... : arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06348.pdf

Mélyebb kérdések

How does the ALTO format address challenges related to irregular shapes and distributions in high-dimensional sparse data

The ALTO format addresses challenges related to irregular shapes and distributions in high-dimensional sparse data by providing a mode-agnostic representation that efficiently encodes tensors in a compact format. This allows for the easy streaming of data from memory, making it amenable to caching and parallel execution. ALTO maps the coordinates of nonzero elements in an N-dimensional space onto a single linearized index, ensuring that neighboring points in space are close to each other on the resulting compact line. By adapting the encoding scheme based on the cardinalities of tensor modes, ALTO reduces storage requirements and improves data locality across all modes of a given sparse tensor. Additionally, ALTO enables workload partitioning at the granularity of nonzero elements, leading to perfectly balanced partitions in terms of workload for efficient parallel execution.

What are potential drawbacks or limitations of adopting a mode-agnostic approach like ALTO for tensor decomposition

While adopting a mode-agnostic approach like ALTO for tensor decomposition offers significant advantages, there are potential drawbacks or limitations to consider:

Complexity: Implementing algorithms with mode-agnostic formats like ALTO may introduce additional complexity compared to traditional approaches that rely on mode-specific structures.

Overhead: The de-linearization (decompression) process required by formats like ALTO can introduce some computational overhead during operations on sparse tensors.

Memory Usage: While ALTO reduces storage requirements compared to traditional formats like COO or CSF, there may still be constraints related to memory usage when dealing with very large datasets or limited memory capacities.

Adaptation Challenges: Adapting existing algorithms designed for specific compressed formats (e.g., CSF) to work effectively with a mode-agnostic format like ALTO may require significant modifications and optimizations.

Performance Trade-offs: Depending on the characteristics of the dataset and algorithm being used, there could be performance trade-offs associated with choosing a mode-agnostic approach over more specialized formats optimized for specific operations.

How might advancements in parallel computing technologies impact the efficiency of algorithms utilizing formats like ALTO

Advancements in parallel computing technologies have the potential to significantly impact the efficiency of algorithms utilizing formats like ALTO:

Improved Scalability: Parallel computing technologies such as multi-core CPUs and GPUs can enhance scalability by allowing algorithms using formats like ALTO to distribute computations across multiple processing units efficiently.

Enhanced Performance: With advancements in parallel computing architectures and techniques such as SIMD (Single Instruction Multiple Data) instructions and task-based parallelism, algorithms leveraging formats like ALTO can achieve higher levels of performance through better utilization of available resources.

Reduced Execution Time: Parallel computing technologies enable concurrent execution of tasks on different parts of data stored using formats like ALTO, leading to reduced overall execution time for complex tensor operations.

4 .Optimized Resource Utilization: Advanced scheduling mechanisms provided by modern parallel computing frameworks can help optimize resource utilization when working with large-scale datasets encoded using formatssuch asALTo
5 .Increased Flexibility: As parallel computing technologies continue evolving , they offer greater flexibilityin howalgorithms utilizingformatslikeALToareimplementedandexecuted,makingit easiertoadapttochanginghardwarearchitecturesandoptimizetheperformanceoftensoroperations