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arxiv: 2605.11333 · v1 · submitted 2026-05-11 · 💻 cs.DC · cs.LG· cs.PF

Recognition: no theorem link

MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces

Srinivas Sridharan , Andy Balogh , Bradford M. Beckmann , Brian Coutinho , Louis Feng , Sheng Fu , Sanshan Gao , Mehryar Garakani
show 20 more authors
Taekyung Heo David Kanter Josh Ladd Ziwei Li Winston Liu Changhai Man Dan Mihailescu Spandan More Joongun Park Ashwin Ramachandran Vinay Ramakrishnaiah Saeed Rashidi Vijay Janapa Reddi Puneet Sharma Phio Tian William Won Hanjiang Wu Huan Xu Jinsun Yoo Tushar Krishna
Authors on Pith no claims yet

Pith reviewed 2026-05-13 01:24 UTC · model grok-4.3

classification 💻 cs.DC cs.LGcs.PF
keywords Chakra execution traceperformance benchmarkingdistributed AI workloadsSW-HW co-designexecution tracesMLCommonsgraph-based representationsimulators and emulators
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The pith

Chakra establishes a standardized graph-based execution trace format to represent distributed AI workloads for benchmarking and co-design.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Chakra as an open ecosystem designed to support observation, reproduction, and optimization of distributed machine learning workloads. Its central element is the Chakra execution trace, a portable graph representation that records compute, memory, and communication operations together with dependencies, timing, and resource limits. This approach addresses the need for consistent methods to analyze production AI systems amid rapid hardware and software changes. By supplying supporting tools for trace collection, generation, and use in simulators and emulators, the work seeks to enable broader interoperability and more efficient software-hardware co-design.

Core claim

The core component of Chakra is an open and interoperable graph-based representation of distributed AI/ML workloads, called Chakra execution trace (ET). These ETs represent key operations, such as compute, memory, and communication, data and control dependencies, timing, and resource constraints. Chakra includes a complementary set of tools and capabilities to enable the collection, analysis, generation, and adoption of Chakra ETs by a broad range of simulators, emulators, and replay tools. Analysis of Chakra ETs collected on production AI clusters and real-world case studies demonstrate its value, with adoption by MLCommons and contributions from industry partners.

What carries the argument

The Chakra execution trace (ET), a graph-based representation that encodes distributed AI/ML workloads through operations, dependencies, timing, and constraints.

If this is right

  • Traces collected from production AI clusters can be analyzed to understand workload behavior.
  • Case studies can validate practical benefits in performance optimization.
  • Multiple simulators and replay tools can integrate the format for consistent benchmarking.
  • Industry partners can contribute to and use a shared representation for co-design efforts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A common trace format could reduce redundant work when moving performance models between different simulation platforms.
  • Patterns visible in standardized traces might identify recurring bottlenecks to guide targeted hardware improvements.
  • The approach could extend to non-AI distributed workloads if the graph structure proves sufficiently general.

Load-bearing premise

The assumption that simulators, emulators, and replay tools will adopt the Chakra ET format and tools to deliver meaningful improvements in benchmarking and co-design.

What would settle it

Demonstration that leading simulation tools continue to rely on proprietary formats and produce equivalent or superior co-design results without using Chakra ETs.

Figures

Figures reproduced from arXiv: 2605.11333 by Andy Balogh, Ashwin Ramachandran, Bradford M. Beckmann, Brian Coutinho, Changhai Man, Dan Mihailescu, David Kanter, Hanjiang Wu, Huan Xu, Jinsun Yoo, Joongun Park, Josh Ladd, Louis Feng, Mehryar Garakani, Phio Tian, Puneet Sharma, Saeed Rashidi, Sanshan Gao, Sheng Fu, Spandan More, Srinivas Sridharan, Taekyung Heo, Tushar Krishna, Vijay Janapa Reddi, Vinay Ramakrishnaiah, William Won, Winston Liu, Ziwei Li.

Figure 1
Figure 1. Figure 1: AI system SW-HW co-design flow [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Chakra Infrastructure Overview. to describe distributed AI workload performance behavior over an AI platform. Analogous to instruction and mem￾ory traces (Ranganathan & Victor), ETs record operator dimensions for compute and communication and their de￾pendencies while avoiding disclosure of model or dataset details. Software organizations can share ETs of internal workloads with hardware vendors, who can i… view at source ↗
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Chakra ET visualization example. broad tasks: analysis, replay and simulation/emulation that are required at different times within the development cycle of AI platforms. The open schema enables interoperability across different stages and diverse open/proprietary tools. 4.1 Trace Analysis Chakra offers a range of open-source tools to help users vi￾sualize, analyze, and consume execution traces. We describ… view at source ↗
Figure 6
Figure 6. Figure 6: Normalized execution time breakdown across workloads for traces collected on the system mentioned in Sec. 5. For each workload, we show measured performance from Kineto (left) and the performance via trace reconstruction through Chakra (right). AllToAll AllGather ReduceScatter AllReduce Collective Communication Type 0.0 0.2 0.4 0.6 0.8 1.0 Total Duration (µs) 1e7 4.1× slower 4.4× slower 1.5× slower 9.7× sl… view at source ↗
Figure 7
Figure 7. Figure 7: Total collective communication runtime comparison at 400 Gb/s and 100 Gb/s InfiniBand. Measured on training Mixtral￾8×22B with 32 GPUs (four HGX-8×H200 nodes, TP/SP=4, EP=8) and the global batch size of 32. open-source tools like Genie (Yoo et al., 2026b) as well as commercial system emulators like Keysight AI Data Center Builder (Keysight Technologies, 2025), which now support the Chakra format for worklo… view at source ↗
Figure 8
Figure 8. Figure 8: GPU memory utilization for different LLM models dur￾ing one training step. Traces are aligned relative to the start of each epoch. Each model and its corresponding parallelization match the first entry (row) in [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Compute characteristics of the Mixtral-8x22-Chakra trace. (a) Most compute kernels complete within 2–102 µs. (b) The majority of nodes have 10–500 parent data dependencies. 5.2 Trace Replay Case Studies Replaying Chakra ETs on real systems allows reproduc￾ing the exact workload behavior either fully (replay both compute and comms operations) or partial replay (replay selective operations). The latter enabl… view at source ↗
Figure 10
Figure 10. Figure 10: Bus bandwidth per iteration when (a) All-Reduce (b) All-to-All (c) mixing All-to-All and All-Reduce in one time span. AllReduce1 AllReduce10 AllReduce2 AllReduce3 AllReduce4 AllReduce5 AllReduce6 AllReduce7 AllReduce8 AllReduce9 AllToAll1 AllToAll10 AllToAll2 AllToAll3 AllToAll4 AllToAll5 AllToAll6 AllToAll7 AllToAll8 AllToAll9 Percentile 100 80 60 40 20 0 Completion Time (ms) 5 10 15 20 25 30 35 40 45 50… view at source ↗
Figure 11
Figure 11. Figure 11: Mixing collectives results of CDF. Result. The experiment revealed a significant performance anomaly when interleaving All-Reduce and All-to-All col￾lectives. While both collectives show stable performance in isolation ( [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Communication time for different network topology and bandwidth with Mixtral 8x7B target. connected. Additionally, we test bandwidths ranging from 75 GB/s to 900 GB/s. The Mixtral 8×7B model serves as the workload for this evaluation. Results [PITH_FULL_IMAGE:figures/full_fig_p011_12.png] view at source ↗
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 14
Figure 14. Figure 14: Distribution of token routing among two expert parallel rank for each model layer. The input has six tokens and the model used is Mixtral 8x7B with 32 layers. 0 4 8 12 16 20 24 28 Model Layer ID 110 120 130 140 150 160 170 180 190 KV Transfer Duration (µs) Send (prefill) Recv (decode) [PITH_FULL_IMAGE:figures/full_fig_p012_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Runtime breakdown of the KV cache transfer for in￾ferencing Llama3-8B between one prefill and decode GPU. The captured trace denotes the per-layer (32 layers for Llama3-8B) send and receive latency between two GPUs. 5.5.3 KV-Cache Transfer In inference, when disaggregating prefill and decode stages on different GPUs (Patel et al., 2024; Zhong et al., 2024; Bambhaniya et al., 2026), it introduces unique po… view at source ↗
read the original abstract

The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed machine learning~(ML) workload behavior in production AI systems and enables efficient software-hardware~(SW-HW) co-design for future systems. We present Chakra, an open and portable ecosystem for performance benchmarking and co-design. The core component of Chakra is an open and interoperable graph-based representation of distributed AI/ML workloads, called Chakra execution trace~(ET). These ETs represent key operations, such as compute, memory, and communication, data and control dependencies, timing, and resource constraints. Additionally, Chakra includes a complementary set of tools and capabilities to enable the collection, analysis, generation, and adoption of Chakra ETs by a broad range of simulators, emulators, and replay tools. We present analysis of Chakra ETs collected on production AI clusters and demonstrate value via real-world case studies. Chakra has been adopted by MLCommons and has active contributions and engagement across the industry, including but not limited to NVIDIA, AMD, Meta, Keysight, HPE, and Scala, to name a few.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Chakra, an open ecosystem for performance benchmarking and co-design of distributed AI/ML workloads. The core is the Chakra execution trace (ET), a graph-based representation capturing compute, memory, communication operations, dependencies, timing, and resource constraints. It provides tools for ET collection, analysis, generation, and integration with simulators, emulators, and replay tools. The paper includes analysis of production traces from AI clusters and real-world case studies, noting adoption by MLCommons and contributions from NVIDIA, AMD, Meta, and other industry players.

Significance. Should the standardized ET format achieve broad adoption, it would offer a significant advance by enabling consistent representation of complex distributed workloads, facilitating cross-tool interoperability, and supporting more effective SW-HW co-design for AI systems. The involvement of MLCommons and multiple vendors strengthens the potential for impact in the field.

major comments (2)
  1. The central claim that Chakra advances benchmarking and co-design relies on the adoption and use by simulators and emulators, yet the manuscript provides no quantitative interoperability test results or adoption metrics to demonstrate this (see Abstract and the section on tools and adoption).
  2. The real-world case studies are mentioned but lack specific quantitative before-and-after metrics on improvements in benchmarking accuracy or co-design outcomes attributable to the Chakra ET format (see the case studies section).
minor comments (2)
  1. The definition of the ET graph structure (nodes, edges, attributes for timing and constraints) would benefit from a formal specification or concrete example diagram in the main text for clarity.
  2. Ensure the related work section includes comparisons to prior execution trace formats such as those used in existing ML performance tools.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, with indications of planned revisions to the next version of the paper.

read point-by-point responses

  1. Referee: The central claim that Chakra advances benchmarking and co-design relies on the adoption and use by simulators and emulators, yet the manuscript provides no quantitative interoperability test results or adoption metrics to demonstrate this (see Abstract and the section on tools and adoption).

    Authors: We acknowledge that the manuscript currently presents adoption primarily through qualitative statements regarding MLCommons standardization and contributions from partners including NVIDIA, AMD, Meta, Keysight, HPE, and Scala. No numerical metrics on the number of integrations or results from formal interoperability tests (e.g., cross-simulator fidelity comparisons) are included. In the revised manuscript we will expand the tools and adoption section with all currently available quantitative indicators of usage and will add descriptions of interoperability validation performed during tool development. Comprehensive cross-tool benchmark suites remain an area of active community development. revision: partial

  2. Referee: The real-world case studies are mentioned but lack specific quantitative before-and-after metrics on improvements in benchmarking accuracy or co-design outcomes attributable to the Chakra ET format (see the case studies section).

    Authors: The case studies section illustrates how Chakra ETs were collected from production clusters and used to drive analysis and co-design decisions. We agree that explicit before-and-after quantitative comparisons would strengthen the claims. In the revision we will augment the case studies with available quantitative results from the trace analyses, including measured improvements in bottleneck identification and any co-design outcomes that can be directly attributed to the standardized representation. revision: yes

Circularity Check

0 steps flagged

No circularity: new format definition with no derivations or self-referential reductions

full rationale

The manuscript introduces Chakra as a new open graph-based execution trace format and supporting ecosystem for AI/ML workload representation. No equations, fitted parameters, predictions, or first-principles derivations appear in the provided text or abstract. The core claim is definitional (the ET format captures ops, dependencies, timing, and constraints) and ecosystem-oriented (tools for collection/analysis plus asserted industry adoption). No step reduces by construction to prior inputs, self-citations, or fitted quantities; the paper is self-contained as a standards proposal without internal logical loops.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the utility of a newly defined graph-based trace format and its adoption by simulators and industry; no numerical free parameters are introduced.

axioms (1)
  • domain assumption Graph-based structures can faithfully capture compute, memory, communication operations, dependencies, timing, and resource constraints in distributed ML systems.
    This is the foundational premise for defining the Chakra ET representation.
invented entities (1)
  • Chakra execution trace (ET) no independent evidence
    purpose: Portable, interoperable graph representation of distributed AI/ML workload behavior including operations, dependencies, timing, and constraints.
    Newly introduced format intended to enable collection, analysis, and use across simulators and emulators.

pith-pipeline@v0.9.0 · 5621 in / 1372 out tokens · 56571 ms · 2026-05-13T01:24:54.212830+00:00 · methodology

discussion (0)

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