Tangram: Hiding GPU Heterogeneity for Efficient LLM Parallelization
Authors:
Yanda Tao,
Pedro F. Silvestre,
Marcel Wagenländer,
Peter Pietzuch
Abstract:
The scale of LLM training jobs requires parallelization planning over large GPU clusters. Due to different GPU types and interconnects added over time, these GPU clusters are increasingly heterogeneous. Automatic LLM parallelizers can search for parallelization plans but face an exploding search space with heterogeneous GPUs. To make search tractable in heterogeneous GPU clusters, parallelizers of…
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The scale of LLM training jobs requires parallelization planning over large GPU clusters. Due to different GPU types and interconnects added over time, these GPU clusters are increasingly heterogeneous. Automatic LLM parallelizers can search for parallelization plans but face an exploding search space with heterogeneous GPUs. To make search tractable in heterogeneous GPU clusters, parallelizers often omit types of parallelism (e.g., expert parallelism) or memory-saving techniques (e.g., ZeRO), which results in worse plans.
We describe Tangram, a system that enables the use of existing heterogeneity-unaware LLM parallelizers in heterogeneous GPU clusters by decoupling parallelization planning from GPU heterogeneity. For this, Tangram exploits two insights: (1) since bulk purchases result in sets of GPUs with similar compute, memory, and connectivity, Tangram can expose such homogeneous GPU islands to existing parallelizers; and (2) parallelizers commonly first partition models and then parallelize partitions. Tangram can compose such model slices, assigned to GPU islands, into work-balanced pipelines for high throughput. Tangram integrates with existing parallelizers through a narrow API, which relies on the enumeration of model-slice/island pairs. Tangram achieves up to 2.3x higher training throughput than current heterogeneous parallelizers (Metis and Sailor) and scales to large GPU clusters by pruning enumerated plans.
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Submitted 15 June, 2026;
originally announced June 2026.
Tempo: Compiled Dynamic Deep Learning with Symbolic Dependence Graphs
Authors:
Pedro F. Silvestre,
Peter Pietzuch
Abstract:
Deep learning (DL) algorithms are often defined in terms of temporal relationships: a tensor at one timestep may depend on tensors from earlier or later timesteps. Such dynamic dependencies (and corresponding dynamic tensor shapes) are difficult to express and optimize: while eager DL systems support such dynamism, they cannot apply compiler-based optimizations; graph-based systems require static…
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Deep learning (DL) algorithms are often defined in terms of temporal relationships: a tensor at one timestep may depend on tensors from earlier or later timesteps. Such dynamic dependencies (and corresponding dynamic tensor shapes) are difficult to express and optimize: while eager DL systems support such dynamism, they cannot apply compiler-based optimizations; graph-based systems require static tensor shapes, which forces users to pad tensors or break-up programs into multiple static graphs.
We describe Tempo, a new DL system that combines the dynamism of eager execution with the whole-program optimizations of graph-based compilation. Tempo achieves this through a declarative programming model with recurrent tensors, which include explicit temporal dimensions. Temporal dimensions can be indexed using symbolic expressions to express dynamic dependencies on past and future tensors. Based on this, Tempo constructs a symbolic dependence graph, which concisely encodes dynamic dependencies between operators, and applies whole-program optimizations, such as algebraic simplifications, vectorization, tiling, and fusion. By tiling dynamic dependencies into static-size blocks, Tempo can also reuse existing static code-generators. It then uses a polyhedral model to find a feasible execution schedule, which includes memory management operations. We show that Tempo achieves a 7$\times$ speedup over JAX for Llama-3.2-3B decoding; for reinforcement learning algorithms, Tempo achieves a 54$\times$ speedup, with 16$\times$ lower peak memory usage.
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Submitted 8 October, 2025; v1 submitted 9 January, 2025;
originally announced January 2025.