# Advanced Comput, Math & Data

Research Highlights

July 2014

## Comm Link

### Framework generates communication-optimal algorithms for contracting distributed tensors; may represent new state of the art

**Results: **Tensor contractions, generalized matrix multiplications
that are time-consuming to calculate, make them among the most
compute-intensive operations in several *ab
initio* computational quantum chemistry methods. In this work, scientists
from Pacific Northwest National Laboratory and The Ohio State University
developed a systematic framework that uses three fundamental communication
operators—recursive broadcast, rotation, and reduction, or RRR,—to derive
communication-efficient algorithms for distributed contraction of arbitrary
dimensional tensors on the IBM Blue Gene/Q Mira supercomputer. The framework automatically
models potential space-performance trade-offs to optimize the communication
costs incurred in executing tensor contractions on supercomputers. The paper
documenting this work, “Communication-optimal Framework for Contracting
Distributed Tensors,” is a SC14 Best Paper award finalist.

Iteration Space and Data Space Mapping for Matrix-Matrix Multiplication on a 2D Torus Network Enlarge Image.

**Why it Matters: **In computational physics and chemistry, tensor
algebra is important because it provides a mathematical framework for
formulating and solving problems related to areas such as fluid mechanics. By
offering a comprehensive framework that automatically generates communication-optimal
algorithms for contracting distributed tensors, redundancy is avoided and the
total computation load is balanced, improving the overall communication costs.
By deconstructing these distributed tensor contractions, the work also afforded
insights into the fundamental building blocks of these widely studied computations.

**Methods: **The researchers characterized distributed tensor
contraction algorithms on tori (mesh circles with wraparound connected in more
than one dimension) networks, defining tensor indices, iteration space, and
their mappings. By mapping the iteration space, they could precisely define
where each computation of a tensor contraction occurs, as well as define the
data that needs to be present in each processor. For each tensor contraction, the
researchers sought an iteration space mapping, a data space mapping, and an
algorithm to minimize the communication cost (per contraction) for a given
amount of memory per processor.

Then, for a given iteration space mapping, their RRR framework identified the fundamental data movement directions required by a distributed algorithm, which also are elemental to the tensor contraction, called “reuse dimensions.” With these reuse dimensions, the framework can compute compatible input and output tensor distributions and systematically generate a contraction algorithm for them using communication operators. In their work, the researchers also showed a cost model that predicted the communication cost for a given iteration space mapping, compatible input and output distribution, and the generated contraction algorithm. The cost model then was used to identify iteration and data space mapping that minimized the overall communication cost.

In their experiments, the researchers showed their framework was scalable up to 16,384 nodes (262,144 cores) on Blue Gene/Q supercomputers. They also demonstrated how their framework improves commutation optimality—even exceeding the Cyclops Tensor Framework, which stands as the current state of the art.

**What’s Next?** In addition to their distributed and symmetric nature,
tensors also might exhibit various forms of sparsity. The team is working on
combining this work with the approach published in “A Framework for Load
Balancing of Tensor Contraction Expressions via Dynamic Task Partitioning,” presented
last year at SC13, to dynamically load balance tensor contractions. The outcome
would be a hybrid approach that exploits the communication efficiency of this
work while dynamically adapting to load imbalances introduced by sparsity.

**Acknowledgments: **This material is based on work supported by the
U.S. Department of Energy’s Office of
Advanced Scientific Computing Research. This research also used resources from
the Argonne Leadership Computing Facility, a DOE Office of Science user facility.** **

**Research Team:** The team includes **Sriram Krishnamoorthy**, a research scientist
with the Advanced Computing, Mathematics, and Data Division’s High Performance Computing
group, as well as Samyam Rajbhandari, Akshay Nikam, Pai-Wei Lai, Kevin Stock,
and P. Sadayappan, all from The Ohio State University, Department of Computer
Science and Engineering.

**References: **

Rajbhandari S, A Nikam, P Lai, K Stock, S Krishnamoorthy, and P Sadayappan. 2014.
“Communication-optimal framework for contracting distributed tensors.”
Presented at: *International Conference
for High Performance Computing, Networking, Storage and Analysis (SC14)*.
November 16-21, 2014, New Orleans, Louisiana (Best Paper Finalist).

Lai
P, K Stock, S Rajbhandari, S Krishnamoorthy, and P Sadayappan. 2013. “A
framework for load balancing of tensor contraction expressions via dynamic task
partitioning.” Presented at: *International
Conference for High Performance Computing, Networking, Storage and Analysis
(SC13)*. November 17-22, 2013, Denver, Colorado.