1,071 research outputs found
Breadth-First Pipeline Parallelism
We introduce Breadth-First Pipeline Parallelism, a novel training schedule
which optimizes the combination of pipeline and data parallelism. Breadth-First
Pipeline Parallelism lowers training time, cost and memory usage by combining a
high GPU utilization with a small batch size per GPU, and by making use of
fully sharded data parallelism. Experimentally, we observed an increase of up
to 43% in training throughput for a 52 billion-parameter model using a small
batch size per GPU compared to Megatron-LM, which would reduce the training
time and cost by the same amount on a large GPU cluster
Dynamic Pipeline: an adaptive solution for big data
The Dynamic Pipelineis a concurrent programming pattern amenable to be parallelized. Furthermore, the number of processing units used in the parallelization is adjusted to the size of the problem, and each processing unit uses a reduced memory footprint. Contrary to other approaches, the Dynamic Pipeline can be seen as ageneralization of the (parallel) Divide and Conquer schema, where systems can be reconfigured depending on the particular instance of the problem to be solved. We claim that the Dynamic Pipelines is useful to deal with Big Data related problems. In particular, we have designed and implemented algorithms for computing graphs parameters as number of triangles, connected components, and maximal cliques, among others. Currently, we are focused on designing and implementing an efficient algorithm to evaluate conjunctive query.Peer ReviewedPostprint (author's final draft
A Fast and Accurate Cost Model for FPGA Design Space Exploration in HPC Applications
Heterogeneous High-Performance Computing
(HPC) platforms present a significant programming challenge,
especially because the key users of HPC resources are scientists,
not parallel programmers. We contend that compiler technology
has to evolve to automatically create the best program variant
by transforming a given original program. We have developed a
novel methodology based on type transformations for generating
correct-by-construction design variants, and an associated
light-weight cost model for evaluating these variants for
implementation on FPGAs. In this paper we present a key
enabler of our approach, the cost model. We discuss how we
are able to quickly derive accurate estimates of performance
and resource-utilization from the design’s representation in our
intermediate language. We show results confirming the accuracy
of our cost model by testing it on three different scientific
kernels. We conclude with a case-study that compares a solution
generated by our framework with one from a conventional
high-level synthesis tool, showing better performance and
power-efficiency using our cost model based approach
- …