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REVIEW 3 major objections 5 minor 41 references

$\lambda$PIC: A callback-centric particle-in-cell framework

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A Python particle-in-cell framework claims to match compiled-code speed while letting users inject custom physics as callbacks at every loop stage.

desk verdict A genuinely useful open-source Python PIC framework whose central performance claim — callbacks cost little — is never actually benchmarked with callbacks attached. read the letter →

arxiv 2607.13507 v2 pith:H35IKYD7 submitted 2026-07-15 physics.comp-ph physics.plasm-ph

Xuesong Geng , Yunwei Cui , Lingang Zhang , Liangliang Ji This is my paper · ORCID
classification physics.comp-phphysics.plasm-ph PACS 52.65.Rr
keywords particle-in-cellcallbackarchitecturePythondynamicloadbalancinglaser-plasmainteractiongraphpartitioningQEDcascades
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper presents λPIC, a particle-in-cell simulation framework written in Python and organized around user callbacks attached to named stages of the time loop. Its central claim is that this flexibility does not cost performance: all compute-intensive operations run in compiled C or just-in-time compiled kernels, while the Python layer only orchestrates and dispatches. The authors verify this by comparing against a mature compiled PIC reference on a uniform plasma benchmark, reporting throughput between roughly 60% and 90% of the reference, and by demonstrating parallel scaling up to 960 cores. If the claim holds, λPIC offers a path to custom physics, diagnostics, and multi-physics coupling without recompiling core code.

What carries the argument

The key mechanism is the callback registry with named stages paired with a hybrid Python/compiled-kernel split. The registry exposes every stage of the PIC loop (initialization, field half-updates, particle push sub-steps, current deposition, QED events, loop end) so user callbacks can read or modify the full simulation state. The performance counterpart is the fused particle update: when no callbacks are registered between the intermediate stages of the particle update, the position push, field interpolation, momentum push, and current deposition are combined into a single C loop, reducing memory-bandwidth pressure and Python dispatch overhead. Graph-based domain decomposition over patches

What would settle it

Run the same uniform-plasma timestep benchmark with a simple one-line callback attached to each exposed stage (position push, field interpolation, momentum push, current deposition) and compare throughput against the no-callback run at the same core count. If throughput drops significantly, the claim that the Python layer is not a bottleneck when callbacks are active would be refuted.

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Extended reading notes

Core claim

λPIC's discovery is that a callback-centric design can decouple extensibility from the static timestep loop without paying a Python-interpreter penalty. The main loop contains no hard-wired output, injection, or diagnostic logic; every stage is a named hook that user-defined Python functions can attach to, receiving the full simulation state as array objects. Performance-critical kernels — particle push, current deposition, field update, and communication — are implemented in C or just-in-time compiled code, and when no callbacks are attached between sub-steps the operations fuse into a single loop. The authors argue that performance is therefore bounded by communication and surface-to-volum

Load-bearing premise

The performance comparison is made on the fused no-callback code path, so the central 'flexibility without performance loss' claim implicitly assumes that attaching Python callbacks to multiple loop stages adds negligible overhead; this is never benchmarked.

Editorial extensions

If this is right

  • Researchers can prototype new kinetic-physics modules, diagnostics, or coupling schemes in Python and run them without recompiling the core solver.
  • In-situ analysis becomes a natural operation: callbacks can compute species-resolved fields, track particles by threshold, or dump reduced diagnostics at arbitrary loop stages.
  • The architecture supports hybrid fluid–PIC models where a callback both reads and writes the current density, a two-way coupling that would normally require restructuring a compiled code.
  • Non-rectangular simulation domains become practical because the patch adjacency graph, not a Cartesian layout, defines the domain; an annular-domain demonstration is included.
  • If the performance claim is correct, the framework can serve as a testbed for new algorithms at close to production speed before any port to GPU or other backends.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not benchmark the callback path itself: the fused kernel is only active when no callbacks are attached, so the 60–90% ratio reflects the no-callback fused kernel. Attaching several callbacks per step will add some overhead; how much remains an open question.
  • The single-node comparison uses one thread per MPI process and no file output. Threaded callbacks and I/O-heavy diagnostics, which are the intended use cases, could change the ratio.
  • The graph-partitioning approach and rank-stable migration suggest a promising route to heterogeneous-node load balancing, but the paper demonstrates only a static mask-based annular domain and a laser-wakefield rebalancing example, not a systematic scaling study of rebalancing cost.
  • The lazy-deletion strategy with dead-slot reuse generalizes beyond PIC: any particle-like simulation with high creation and annihilation rates could adopt it.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents λPIC, an electromagnetic particle-in-cell framework whose distinctive design is a callback-centric architecture: the main timestep loop exposes all stages (field updates, particle push stages, current deposition, QED events, etc.) as named hooks to which users attach arbitrary Python functions operating on the full simulation state. Performance-critical kernels are implemented in C and Numba; fields and particles are NumPy arrays; MPI parallelism is combined with graph-based domain decomposition for dynamic load balancing and non-rectangular domains. The paper reports verification against EPOCH on numerical heating and a strongly nonlinear laser-plasma interaction, strong/weak scaling up to 960 cores, a single-node throughput comparison with EPOCH, and three application examples (species-resolved fields, annular domains, hybrid fluid-PIC coupling).

Significance. If the central claims hold, λPIC would be a useful contribution to the PIC software landscape: it addresses a real tension between extensibility and performance, and it offers a concrete open-source platform for prototyping new physics and in-situ diagnostics without recompiling a monolithic core. The paper's strengths include direct comparison with a mature compiled code (EPOCH), a public GPL-licensed implementation, and demonstration of non-trivial applications (species-resolved fields, annular domain, hybrid coupling) that would be difficult in conventional PIC frameworks. The architecture's claim of 'flexibility without performance loss' is, however, currently supported only for the fused, callback-free execution path; the cost of the framework's defining feature—attaching callbacks at intermediate stages—is never measured. Likewise, the dynamic load-balancing demonstrations are purely qualitative. These gaps are fixable but require additional benchmarking before the paper's main assertions are fully established.

major comments (3)
  1. [§2.3, §5.2] The central performance claim is measured only on the fused, callback-free path. §2.3 states the unified pusher is activated only 'when no callbacks are registered between the intermediate stages of the particle update', and §5.2's EPOCH throughput comparison uses a timed loop with no file output and therefore no intermediate-stage callbacks. Attaching a callback at an intermediate stage (e.g., after current deposition, as in the §6.1 species-resolved example) disables the fused C kernel, forcing separate position-push, interpolation, momentum-push, and current-deposition passes and adding Python dispatch. The likely cost is not just Python overhead but additional memory passes over the particle arrays. The paper never benchmarks this. I request a benchmark of the same thermal-plasma problem with a no-op callback attached to one or more intermediate stages, comparing against the no-callb
  2. [§4.1, §4.2] The verification is predominantly qualitative. Fig. 3 reports energy drift curves that are 'consistent with' EPOCH, but no quantitative error metric is given (e.g., RMS deviation between the two codes, final drift values, or convergence order with resolution and particles-per-cell). Fig. 4 compares density maps by eye at three times. The text uses the word 'confirming' (§4.2), which is stronger than the presented evidence. For a code paper, I would like at least one quantitative comparison: for the heating benchmark, a convergence table or error norm; for the laser-plasma benchmark, a defined difference measure between the λPIC and EPOCH density fields, or a threshold for agreement.
  3. [§5.3, §2.4] Dynamic load balancing is advertised as a key capability, but the demonstration is not quantitative. Fig. 7 shows partition snapshots at three times and a graph-partitioning illustration, but there is no measured load-imbalance time series, no comparison of wall-clock time with rebalancing on versus off, and no report of the overhead of graph partitioning and patch migration. The statement that patches 'are redistributed to ensure even computational load' is therefore not backed by data. A simple metric—e.g., per-rank particle-count variance or per-step time before/after rebalancing—would make the claim testable and would also let readers judge whether the e/2 and 3/π threshold-relaxation heuristic behaves as intended.
minor comments (5)
  1. [Fig. 5] The axes in Fig. 5 are unclear: the strong-scaling panel appears to label x-axis 'MPI Ranks' while the text describes core counts up to 960. Please add an explicit statement that each rank uses 96 OpenMP threads and label the axes consistently (total cores or ranks).
  2. [§2.5] The adaptive rebalancing threshold relaxation factors e/2 and 3/π are said to avoid resonance with periodic load fluctuations, but no sensitivity study or reference supports this. A short numerical test with alternative factors, or a reference to a rationale, would avoid the appearance of arbitrary tuning.
  3. [§6.3] The hybrid fluid-PIC comparison is again qualitative ('at a qualitative level', 'qualitatively consistent'). Since the fluid closure is an approximation, a quantitative measure (e.g., correlation of current patterns or phase-space moments) would make this demonstration more convincing.
  4. [§5.1] The sentence 'the performance of λPIC is bounded by the same communication and surface-to-volume effects that limit pure compiled PIC codes' is stronger than what is measured: the data support this on the fused, callback-free path only. Please qualify the statement to reflect the scope of the benchmark.
  5. [General] There are several formatting issues: '6.7×107' and '10 1' should use proper superscripts/exponents; the laser envelope 'Sin2' should be 'sin^2'; and some equations display with missing space in exponents. A careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation chain; external EPOCH benchmarks underpin performance claims; callback-path gap is a coverage limitation, not circularity.

full rationale

The paper makes no physical prediction derived from fitted parameters: the performance and correctness claims rest on direct comparison with EPOCH (§4.1, §4.2, §5.2), standard analytic/numerical methods (Boris pusher, Esirkepov deposition, Yee scheme), and targeted scaling benchmarks. The unified-pusher optimization is explicitly conditional on having no intermediate-stage callbacks (§2.3, §3.3), and the EPOCH comparison is run on that fused path; this is an unmeasured performance gap for the callback-attached regime, but it is a missing benchmark, not a circular reduction. The Fig. 8 species-resolved field check is an internal consistency test relying on Maxwell linearity, and is not used to validate an externally predictive claim. The e/2 and 3/π load-balance threshold factors are stated heuristics, not results derived from the data they govern. The only self-citation ([29]) appears in an application example and is illustrative, not load-bearing for the framework's central claims. No self-definitional fit, renamed empirical result, or imported uniqueness theorem was found.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The paper's central claims are architectural and benchmark-based; no new physical theory is derived. The free parameters are only heuristic tuning values. Axioms are standard PIC numerical methods borrowed from literature.

free parameters (2)
  • Rebalance threshold relaxation factors (e/2, 3/π) = e/2 ≈ 1.359, 3/π ≈ 0.955
    Ad hoc irrational multipliers in §2.5 chosen 'to avoid resonance with periodic load fluctuations'; they tune how often load balancing triggers but are not fitted to physical data.
  • prune() extra_buff = not specified in paper
    Buffer fraction retained after particle array compaction (§2.1); a user-configurable memory/performance trade-off, not physically constrained.
assumptions (6)
  • standard math Yee staggered grid + leapfrog time integration with CFL condition is stable and accurate for the electromagnetic PIC solve.
    §3.2; standard FDTD discretization from [35].
  • standard math Boris pusher conserves momentum magnitude for relativistic particles.
    §3.1; standard algorithm [5].
  • standard math Esirkepov current deposition preserves discrete charge conservation.
    §3.1; from [15].
  • domain assumption QED emission and pair production are accurately modeled by local constant field approximation with event-based Monte Carlo and optical depth.
    §3.4; standard approximation [14,30], not derived here.
  • domain assumption CPML absorbing boundaries effectively absorb outgoing radiation in all presented tests.
    §3.2; ported from EPOCH [1,3].
  • domain assumption EPOCH is a valid reference for correctness and performance; agreement with EPOCH implies correctness.
    All verification sections compare to EPOCH; no independent analytic solution is used.

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Cite this review

Pith. "Pith review of $\lambda$PIC: A callback-centric particle-in-cell framework." pith.science (2026). https://pith.science/paper/H35IKYD7

@misc{pith2026260713507,
  author       = {Pith},
  title        = {Pith review of: $\lambda$PIC: A callback-centric particle-in-cell framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H35IKYD7}},
  note         = {Machine review of arXiv:2607.13507}
}
abstract

We present $\lambda$PIC, a Python-based electromagnetic particle-in-cell framework built around a callback-centric architecture. Existing PIC codes typically tie high performance to static, pre-compiled timestep loops, hindering implementation of custom physics, diagnostics, or output logic. $\lambda$PIC breaks this coupling by exposing every stage of the loop as a named stage (hook), permitting attaching arbitrary Python functions that operate on the full simulation state, enabling custom algorithms and in-situ analysis without modifying the core algorithms. Under this flexible framework, performance-critical kernels are written in C extensions and Numba, fields and particles are stored in NumPy arrays, and MPI parallelism is paired with graph partitioning to support dynamic load balancing and non-rectangular domains. Although $\lambda$PIC is designed as general-purpose, it has special focus on intense laser-plasma interactions. Future work will extend the framework to GPU acceleration and additional physics modules including implicit solvers and nuclear physics.

Figures

Figures reproduced from arXiv: 2607.13507 by the authors.

Figure 1
Figure 1. Class diagram of the 𝜆PIC core architecture. A top-level Simulation object composes the Patches container, physics modules (MaxwellSolver and Pusher for example), and the callback registry (SaveFieldsToHDF5 and GaussianLaser for example). Patches contains Patch instances, each holding field data, PML boundaries, and particle data. Not all components and relationships are shown for clarity. container aggregates spati… view at source ↗
Figure 2
Figure 2. Graph-based domain decomposition for a 32 × 32 patch grid on eight MPI ranks. (a) Uniform density and (b) plasma slab density profile with boundaries denoted by red￾dashed line. Axes are patch indices 𝑖𝑥 and 𝑖 𝑦 ; color of the patch denotes the MPI rank. The graph is constructed directly from the neighbor arrays. Each patch is a vertex, and an edge connects adjacent patches. The partitioner is invoked with the conti… view at source ↗
Figure 3
Figure 3. Normalized energy drift in a 2D uniform thermal plasma (𝑛𝑒 = 10 𝑛𝑐 , 𝑇 = 1 keV). Solid curves show 𝜆PIC results for two spatial resolutions and two PPC counts; dashed curves show the corresponding EPOCH results. between the electron and positron according to the pair￾energy distribution and the two new particles are inserted into the corresponding species arrays. Both modules operate via an event-based Monte Carlo s… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: shows the electron density 𝑛𝑒∕𝑛𝑐 at 𝑡 = 72, 115, and 133 fs. At the earliest time the laser front has pushed the plasma surface into a curved density spike, and by 𝑡 = 115 fs a pronounced channel has formed with filamentary structure along its walls. At 𝑡 = 133 fs the …
Figure 6
Figure 6. Figure 6: Timestep throughput (left axis) and 𝜆PIC-to￾EPOCH throughput ratio (right axis) on a 2D uniform thermal plasma (1024 × 1024 grid, 32 particles per cell per species, 𝑛𝑒 = 10 𝑛𝑐 , 𝑇 = 1 keV, periodic boundaries). Both codes use 𝑁 MPI processes with one thread each on a s…
Figure 5
Figure 5. Figure 5: Strong (a) and weak (b) scaling. Black circles denote measured data, the black dashed line denotes ideal scaling, and red squares denote parallel efficiency (right-hand axis). 256 macro-particles per cell per species. Load balancing is disabled so that the scaling refl…
Figure 7
Figure 7. Figure 7: Dynamic load balancing of a laser-wakefield acceleration simulation with 8 MPI ranks. Electron density 𝑛𝑒∕𝑛𝑒0 is shown in grayscale, with ranks shown in different colors. (a) 𝑡 = 36 fs, (b) 𝑡 = 90 fs, (c) 𝑡 = 180 fs. 𝜆PIC achieves a substantial fraction of EPOCH’s perf…
Figure 8
Figure 8. Figure 8: Species-resolved reconstruction of the longitudinal electric field for a relativistic electron bunch traversing an overdense plasma slab. (a) 𝐸𝑥 driven by the plasma-slab currents alone. (b) 𝐸𝑥 driven by the bunch currents alone. (c) Sum of (a) and (b). (d) Total 𝐸𝑥 fr…
Figure 9
Figure 9. Figure 9: Annular simulation domain created with a mask function on 8 ranks. The domain patches are colored by MPI ranks. Electron density (grayscale) is a half-ring in the upper domain for demonstration of load-balancing. the background as a fluid while retaining a kinetic desc…
Figure 10
Figure 10. Figure 10: Relativistic electron beam transport in a high￾density plasma. (a) Transverse current 𝐽𝑦 with beam density 𝑛beam for the hybrid fluid-PIC simulation and (b) for the full-PIC simulation. (c) Transverse phase space (𝑟, 𝐮 ⋅ 𝐫̂) of the beam electrons for the hybrid fluid-…

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Reference graph

Works this paper leans on

41 extracted references · 12 canonical work pages

  1. [1]

    Contemporaryparticle-in-cellapproachto laser-plasma modelling

    Arber, T.D., Bennett, K., Brady, C.S., Lawrence-Douglas, A., Ram- say,M.G.,Sircombe,N.J.,Gillies,P.,Evans,R.G.,Schmitz,H.,Bell, A.R.,Ridgers,C.P.,2015. Contemporaryparticle-in-cellapproachto laser-plasma modelling. Plasma Physics and Controlled Fusion 57, 113001. doi:10.1088/0741-3335/57/11/113001

    doi:10.1088/0741-3335/57/11/113001 2015

  2. [2]

    Efficient data layouts for a three-dimensional electrostatic Particle-in-Cell code

    Barsamian, Y., Hirstoaga, S.A., Violard, É., 2018. Efficient data layouts for a three-dimensional electrostatic Particle-in-Cell code. Journal of Computational Science 27, 345–356. doi:10.1016/j.jocs. 2018.06.004

    doi:10.1016/j.jocs 2018

  3. [3]

    A perfectly matched layer for the absorption of electromagnetic waves

    Berenger, J.P., 1994. A perfectly matched layer for the absorption of electromagnetic waves. Journal of Computational Physics 114, 185–

    1994

  4. [4]

    EMPIRE-PIC: A Performance Portable Un- structured Particle-in-Cell Code

    Bettencourt,M.T.,Brown,D.A.S.,Cartwright,K.L.,Cyr,E.C.,Glusa, C.A., Lin, P.T., Moore, S.G., McGregor, D.A.O., Pawlowski, R.P., Phillips, E.G., Roberts, N.V., Wright, S.A., Maheswaran, S., Jones, J.P., Jarvis, S.A., 2021. EMPIRE-PIC: A Performance Portable Un- structured Particle-in-Cell Code. Communications in Computational Physics 30, 1232–1268. doi:10.4...

    work page doi:10.4208/cicp.oa-2020-0261 2021

  5. [5]

    Relativistic plasma simulation-optimization of a hybrid code, in: Proceedings of the 4th Conference on Numerical Simulation of Plasmas, Naval Res

    Boris, J., 1970. Relativistic plasma simulation-optimization of a hybrid code, in: Proceedings of the 4th Conference on Numerical Simulation of Plasmas, Naval Res. Lab., Washington, D.C. X.S. Geng et al.:Preprint submitted to ElsevierPage 10 of 11 𝜆PIC: A callback-centric PIC framework

    1970

  6. [6]

    doi:10.48550/arXiv.2511.09950,arXiv:2511.09950

    Brogren, F., Olofsson, C., Magnusson, J., Gonoskov, A., 2026.𝜋- PIC: A framework for modular particle-in-cell developments and simulations. doi:10.48550/arXiv.2511.09950,arXiv:2511.09950

    work page doi:10.48550/arxiv.2511.09950 2026

  7. [7]

    C. K. Birdsall, A.B.L., 1991. Plasma Physics via Computer Simula- tion

    1991

  8. [8]

    Two- Dimensional Hybrid Model for High-Current Electron Beam Trans- port in a Dense Plasma

    Cao, L., Wang, H., Zhang, H., Liu, Z., Wu, J., Li, B., 2014. Two- Dimensional Hybrid Model for High-Current Electron Beam Trans- port in a Dense Plasma. Plasma Science and Technology 16, 1007–

    2014

Show all 41 references
  1. [9]

    Fields of Particles and Beams Exiting a Conduc- tor

    Carron, N.J., 2000. Fields of Particles and Beams Exiting a Conduc- tor. JournalofElectromagneticWavesandApplications28,147–183. doi:10.1163/156939300X01391

    2000 doi

  2. [10]

    Tunable synchrotron-like radiation from centimeter scale plasma channels

    Chen, M., Luo, J., Li, F.Y., Liu, F., Sheng, Z.M., Zhang, J., 2016. Tunable synchrotron-like radiation from centimeter scale plasma channels. Light: Science & Applications 5, e16015–e16015. doi:10. 1038/lsa.2016.15

    2016

  3. [11]

    Smilei:Acollaborative, open-source, multi-purpose particle-in-cell code for plasma simulation

    Derouillat, J., Beck, A., Pérez, F., Vinci, T., Chiaramello, M., Grassi, A., Flé, M., Bouchard, G., Plotnikov, I., Aunai, N., Dargent, J., Riconda,C., Grech,M.,2018. Smilei:Acollaborative, open-source, multi-purpose particle-in-cell code for plasma simulation. Computer Physics...

    2018 doi

  4. [12]

    Dy- namic Load Balancing of Plasma and Flow Simulations, in: 2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algo- rithms for Large-Scale Systems (scalA), pp

    Diamond, G., Smith, C.W., Yoon, E., Shephard, M.S., 2018. Dy- namic Load Balancing of Plasma and Flow Simulations, in: 2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algo- rithms for Large-Scale Systems (scalA), pp. 73–80. doi:10.1109/ ScalA.2018.00013

    2018

  5. [13]

    Diamond, G., Smith, C.W., Zhang, C., Yoon, E., Shephard, M.S.,

  6. [14]

    Monte Carlo calculations ofpairproductioninhigh-intensitylaser–plasmainteractions

    Duclous, R., Kirk, J.G., Bell, A.R., 2010. Monte Carlo calculations ofpairproductioninhigh-intensitylaser–plasmainteractions. Plasma Physics and Controlled Fusion 53, 015009. doi:10.1088/0741-3335/ 53/1/015009

    2010 doi

  7. [15]

    Exact charge conservation scheme for Particle-in-Cell simulation with an arbitrary form-factor

    Esirkepov, T.Zh., 2001. Exact charge conservation scheme for Particle-in-Cell simulation with an arbitrary form-factor. Computer Physics Communications 135, 144–153. doi:10.1016/S0010-4655(00) 00228-9

    2001 doi

  8. [16]

    OSIRIS: A Three-Dimensional, Fully Relativistic Particle in Cell Code for Modeling Plasma Based Accelerators, in: Sloot, P.M.A., Hoekstra, A.G., Tan, C.J.K., Dongarra, J.J

    Fonseca, R.A., Silva, L.O., Tsung, F.S., Decyk, V.K., Lu, W., Ren, C., Mori, W.B., Deng, S., Lee, S., Katsouleas, T., Adam, J.C., 2002. OSIRIS: A Three-Dimensional, Fully Relativistic Particle in Cell Code for Modeling Plasma Based Accelerators, in: Sloot, P.M.A., Hoekstra, A....

    2002 doi

  9. [17]

    The Plasma Simu- lation Code: A modern particle-in-cell code with patch-based load- balancing

    Germaschewski, K., Fox, W., Abbott, S., Ahmadi, N., Maynard, K., Wang, L., Ruhl, H., Bhattacharjee, A., 2016. The Plasma Simu- lation Code: A modern particle-in-cell code with patch-based load- balancing. Journal of Computational Physics 318, 305–326. doi:10. 1016/j.jcp.2016.05.013

    2016

  10. [18]

    Software:PracticeandExperience53,115–141

    Gruber, B.M., Amadio, G., Blomer, J., Matthes, A., Widera, R., Bussmann,M.,2023.LLAMA:Thelow-levelabstractionformemory access. Software:PracticeandExperience53,115–141. doi:10.1002/ spe.3077

    2023

  11. [19]

    Computer Simulation Using Particles

    Hockney, R.W., 2021. Computer Simulation Using Particles. CRC Press

    2021

  12. [20]

    SoAx: A generic C++ Structure of Arrays for handling particles in HPC codes

    Homann, H., Laenen, F., 2018. SoAx: A generic C++ Structure of Arrays for handling particles in HPC codes. Computer Physics Communications 224, 325–332. doi:10.1016/j.cpc.2017.11.015

    2018 doi

  13. [21]

    A fast and high quality multi- level scheme for partitioning irregular graphs

    Karypis, G., Kumar, V., 1998. A fast and high quality multi- level scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing 20, 359–392. doi:10.1137/S1064827595287997, arXiv:https://doi.org/10.1137/S1064827595287997

    1998 doi

  14. [22]

    Numba: A LLVM-based PythonJITcompiler,in:ProceedingsoftheSecondWorkshoponthe LLVM Compiler Infrastructure in HPC, Association for Computing Machinery, New York, NY, USA

    Lam, S.K., Pitrou, A., Seibert, S., 2015. Numba: A LLVM-based PythonJITcompiler,in:ProceedingsoftheSecondWorkshoponthe LLVM Compiler Infrastructure in HPC, Association for Computing Machinery, New York, NY, USA. pp. 1–6. doi:10.1145/2833157. 2833162

    2015 doi

  15. [23]

    Effects of the spatial grid in simulation plasmas

    Langdon, A.B., 1970. Effects of the spatial grid in simulation plasmas. JournalofComputationalPhysics6,247–267. doi:10.1016/ 0021-9991(70)90024-0

    1970

  16. [24]

    Le, A., Stanier, A., Yin, L., Wetherton, B., Keenan, B., Albright, B.,

  17. [25]

    Accelera- tionoftheparticle-in-cellcodeOsiriswithgraphicsprocessingunits

    Lee, R.P., Pierce, J.R., Miller, K.G., Almanza, M., Tableman, A., Decyk,V.K.,Fonseca,R.A.,Alves,E.P.,Mori,W.B.,2025. Accelera- tionoftheparticle-in-cellcodeOsiriswithgraphicsprocessingunits. Journal of Plasma Physics 91, E8. doi:10.1017/S0022377824001569

    2025 doi

  18. [26]

    Lehe, R., Kirchen, M., Andriyash, I.A., Godfrey, B.B., Vay, J.L.,

  19. [27]

    Multistage Coupling of Laser-Wakefield Acceler- ators with Curved Plasma Channels

    Luo, J., Chen, M., Wu, W.Y., Weng, S.M., Sheng, Z.M., Schroeder, C.B., Jaroszynski, D.A., Esarey, E., Leemans, W.P., Mori, W.B., Zhang, J., 2018. Multistage Coupling of Laser-Wakefield Acceler- ators with Curved Plasma Channels. Physical Review Letters 120, 154801. doi:10.1103...

    2018 doi

  20. [28]

    Three-dimensional electromagnetic relativistic particle-in-cell code VLPL (Virtual Laser Plasma Lab)

    Pukhov, A., 1999. Three-dimensional electromagnetic relativistic particle-in-cell code VLPL (Virtual Laser Plasma Lab). Journal of Plasma Physics 61, 425–433. doi:10.1017/S0022377899007515

    1999 doi

  21. [29]

    Transi- tion RadiationField Enhanced LaserProton Acceleration Employing Near-Critical-Density Foam

    Qin, C.Y., Geng, X.S., Zhang, H., Yu, L.H., Zhang, L.G., Dai, Y.Z., Wang, J., Zhang, B.W., Guo, X.J., Xu, D.R., Xu, S., Ding, C.L., Xu, Y., Liu, Y.Q., Wang, C., Shi, B.N., Zhang, Z.X., Liu, X.Y., Leng, Y.X., Liang, X.Y., Shen, B.F., Ji, L.L., Li, R.X., 2026. Transi- tion Radia...

    2026

  22. [30]

    Modelling gamma-ray photon emission and pair production in high-intensity laser–matter interactions

    Ridgers, C.P., Kirk, J.G., Duclous, R., Blackburn, T.G., Brady, C.S., Bennett, K., Arber, T.D., Bell, A.R., 2014. Modelling gamma-ray photon emission and pair production in high-intensity laser–matter interactions. JournalofComputationalPhysics260,273–285. doi:10. 1016/j.jcp.2...

    2014

  23. [31]

    Quantum effects of the interaction of elementary particleswithanintenseelectromagneticfield.JournalofSovietLaser Research 6, 497–617

    Ritus, V.I., 1985. Quantum effects of the interaction of elementary particleswithanintenseelectromagneticfield.JournalofSovietLaser Research 6, 497–617. doi:10.1007/BF01120220

    1985 doi

  24. [32]

    BLAST-WarpX/warpx: 26.06

    Vay, J.L., Acciarri, M., Almgren, A., Amorim, L.D., Andriyash, I., Angus, J.R., Antoun, T., Belkin, D., Bizzozero, D., Blelly, A., Clark, S.E., Dammak, E., Fedeli, L., Formenti, A., Garten, M., Ge, L., Giacomel,L.,Gott,K.,Groenewald,R.E.,Grote,D.,Gu,J.,Harrison, C.,Haseeb,M.,H...

    2026

  25. [33]

    Vranic, M., Grismayer, T., Martins, J.L., Fonseca, R.A., Silva, L.O.,

  26. [34]

    Hybrid PIC–fluid simulations for fast electron transport in a silicon target

    Yang, X.H., Chen, Z.H., Xu, H., Ma, Y.Y., Zhang, G.B., Zou, D.B., Shao, F.Q., 2023. Hybrid PIC–fluid simulations for fast electron transport in a silicon target. Matter and Radiation at Extremes 8, 035901. doi:10.1063/5.0137973

    2023 doi

  27. [35]

    Numericalsolutionofinitialboundaryvalueproblems involvingmaxwell’sequationsinisotropicmedia

    Yee,K.,1966. Numericalsolutionofinitialboundaryvalueproblems involvingmaxwell’sequationsinisotropicmedia. IEEETransactions on Antennas and Propagation 14, 302–307. doi:10.1109/TAP.1966. 1138693. X.S. Geng et al.:Preprint submitted to ElsevierPage 11 of 11

    1966 doi

  28. [200]

    doi:10.1006/jcph.1994.1159

    1994

  29. [1012]

    doi:10.1088/1009-0630/16/11/03

    doi

  30. [2015]

    Computer Physics Communications 191, 65–73

    Particle merging algorithm for PIC codes. Computer Physics Communications 191, 65–73. doi:10.1016/j.cpc.2015.01.020

    2015 doi

  31. [2016]

    Computer Physics Communications 203, 66–82

    A spectral, quasi-cylindrical and dispersion-free Particle-In- Cell algorithm. Computer Physics Communications 203, 66–82. doi:10.1016/j.cpc.2016.02.007

    2016 doi

  32. [2021]

    Journal of Parallel and Distributed Com- puting 157, 1–12

    PUMIPic: A mesh-based approach to unstructured mesh Particle-In-Cell on GPUs. Journal of Parallel and Distributed Com- puting 157, 1–12. doi:10.1016/j.jpdc.2021.06.004

    2021 doi

  33. [2023]

    Physics of Plasmas 30, 063902

    Hybrid-VPIC:Anopen-sourcekinetic/fluidhybridparticle-in- cell code. Physics of Plasmas 30, 063902. doi:10.1063/5.0146529

    doi

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.