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US 2026/0037307 A1Published application
Hardware-aware scheduling and data orchestration for balanced llm training on heterogeneous gpu clusters
AssigneeSamsung Electronics Co., Ltd.
InventorsHanqiu CHEN, Andrew CHANG
Life of this patent
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Abstract
A scheduling system is disclosed. The scheduling system may include a simulator to process information regarding a heterogeneous computing system. An intra-node scheduler may determine whether individual nodes should use a tensor parallel approach or a data parallel approach. An inter-node scheduler may schedule operations between the nodes. An evaluator may evaluate a performance of the heterogeneous computing system.
Claims20
A scheduling system, comprising:
- a simulator to process an information regarding a heterogeneous computing system and to generate a first output;
- an intra-node scheduler to schedule operations within a first node and within a second node based on the first output of the simulator and to generate a second output;
- an inter-node scheduler to schedule operations between the first node and the second node based on the second output and to generate a third output;
- and
- an evaluator to evaluate a performance of the heterogeneous computing system based on the third output,
- wherein the heterogeneous computing system includes:
- the first node, wherein the first node includes a first processing element including a first local memory;
- and
- the second node, wherein the second node includes a second processing element including a second local memory,
- wherein the first node includes a first capability, and
- wherein the second nodes includes a second capability, the second capability different from the first capability.
8 more elements
A method, comprising:
- determining a memory report for a heterogeneous computing system;
- assigning a first node of the heterogeneous computing system to use a first tensor parallel approach or a first data parallel approach based at least in part on the memory report;
- assigning a second node of the heterogeneous computing system to use a second tensor parallel approach or a second data parallel approach based at least in part on the memory report;
- scheduling operations between the first node and the second node;
- and
- evaluating a performance of the heterogeneous computing system based at least in part on the operations scheduled between the first node and the second node,
- wherein the heterogeneous computing system includes:
- the first node, wherein the first node includes a first processing element including a first local memory;
- and
- the second node, wherein the second node includes a second processing element including a second local memory,
- wherein the first node includes a first capability, and
- wherein the second nodes includes a second capability, the second capability different from the first capability.
9 more elements
A system, comprising a non-transitory storage medium, the non-transitory storage medium having stored thereon instructions that, when executed by a machine, result in:
- determining a memory report for a heterogeneous computing system;
- assigning a first node of the heterogeneous computing system to use a first tensor parallel approach or a first data parallel approach based at least in part on the memory report;
- assigning a second node of the heterogeneous computing system to use a second tensor parallel approach or a second data parallel approach based at least in part on the memory report;
- scheduling operations between the first node and the second node;
- and
- evaluating a performance of the heterogeneous computing system based at least in part on the operations scheduled between the first node and the second node,
- wherein the heterogeneous computing system includes:
- the first node, wherein the first node includes a first processing element including a first local memory;
- and
- the second node, wherein the second node includes a second processing element including a second local memory,
- wherein the first node includes a first capability, and
- wherein the second nodes includes a second capability, the second capability different from the first capability.
9 more elements
Dependencies are inferred from the claim text.