Minimizing Communication in Linear Algebra (0905.2485v1)

Abstract: In 1981 Hong and Kung proved a lower bound on the amount of communication needed to perform dense, matrix-multiplication using the conventional $O(n^3)$ algorithm, where the input matrices were too large to fit in the small, fast memory. In 2004 Irony, Toledo and Tiskin gave a new proof of this result and extended it to the parallel case. In both cases the lower bound may be expressed as $\Omega$(#arithmetic operations / $\sqrt{M}$), where M is the size of the fast memory (or local memory in the parallel case). Here we generalize these results to a much wider variety of algorithms, including LU factorization, Cholesky factorization, $LDL^T$ factorization, QR factorization, algorithms for eigenvalues and singular values, i.e., essentially all direct methods of linear algebra. The proof works for dense or sparse matrices, and for sequential or parallel algorithms. In addition to lower bounds on the amount of data moved (bandwidth) we get lower bounds on the number of messages required to move it (latency). We illustrate how to extend our lower bound technique to compositions of linear algebra operations (like computing powers of a matrix), to decide whether it is enough to call a sequence of simpler optimal algorithms (like matrix multiplication) to minimize communication, or if we can do better. We give examples of both. We also show how to extend our lower bounds to certain graph theoretic problems. We point out recently designed algorithms for dense LU, Cholesky, QR, eigenvalue and the SVD problems that attain these lower bounds; implementations of LU and QR show large speedups over conventional linear algebra algorithms in standard libraries like LAPACK and ScaLAPACK. Many open problems remain.

Communication-Efficient Algorithms in Linear Algebra

The paper "Minimizing Communication in Linear Algebra" by Ballard et al. addresses the fundamental challenge of minimizing communication in linear algebra computations. Rather than focusing solely on arithmetic operations, the authors meticulously examine communication costs, which include data movement across memory hierarchies and processors. This inquiry is pivotal for executing efficient linear algebra computations on modern architectures where bandwidth and latency often dominate computational costs.

Major Contributions

The paper generalizes earlier results by Hong and Kung and extends Irony et al.'s proof to establish communication lower bounds for a wide array of dense and sparse linear algebra algorithms. Notably, the authors derive these bounds for operations such as LU, Cholesky, and QR factorizations, along with eigenvalue and singular value computations. The foundational result is that for many direct linear algebra methods, the communication cost is at least $\Omega(\text{\#arithmetic\_operations}/\sqrt{M})$, where $M$ is the fast memory size.

Irony et al.'s lattice-based proof is adapted to calculate bounds for both sequential and parallel algorithms. By analyzing the computational lattice of index triplets $(i, j, k)$ corresponding to matrix entries and operations, the authors provide detailed proofs of how many operations can be performed within memory constraints during computations involving data access patterns.

The paper also ventures beyond individual operations to explore lower bounds for compositions of operations, considering whether optimal algorithms for each operation suffice or if a holistic communication-minimized strategy is warranted. Additionally, it makes strides in reformulating these bounds for algorithms applied to graph-theoretic problems.

Numerical Outcomes and Implementations

Ballard et al. illustrate scenarios where newly proposed algorithms for LU and QR factorization achieve remarkable speedups over traditional implementations in LAPACK and ScaLAPACK. Their proposed methods attain or approach the derived communication lower bounds, outperforming existing routines that do not minimize communication optimally.

For example, in dense matrix computations, the proposed blocked algorithms reduce the communication compared to conventional methods. Specifically, they improve implementations in LAPACK for sequential algorithms and ScaLAPACK for parallel settings by avoiding redundant data movement, thereby enhancing overall processing efficiency.

Theoretical and Practical Implications

The theoretical framework provided holds critical implications for the development of highly efficient linear algebra software, especially for problems where input/output communication is a bottleneck. Given the asymptotic nature of the derived bounds, they encourage the design and analysis of "communication-reducing" algorithms for both known and novel matrix operations, challenging practitioners to strive for attainable lower-bound communication costs.

Practically, achieving these bounds could significantly optimize large-scale computations relevant to scientific simulations, machine learning, and data science. The research emphasizes exploring architecture-specific algorithm adjustments that account for increased memory levels and processor distributions.

Future Directions

One compelling future topic may be further aligning these theoretical insights with multi-layered memory hierarchies inherently present in contemporary computational clusters. Further work could focus on derived bounds in more complex data structures like dynamic matrices and adaptive algorithms used in real-time data processing.

Research on sparse, structured matrices remains an open field with scope for breakthroughs in exploiting inherent data patterns to reduce communication overheads. Furthermore, integration with graph-theoretic algorithms paves the way for advancing computational graph analytics.

In conclusion, this paper offers crucial insights into the symbiosis of arithmetic operations and communication in linear algebra, laying a groundwork for generating high-performance computational routines compatible with the evolving landscape of high-performance computing architectures.