The need for modern computing paradigm: Science applied to computing

J\'anos V\'egh

REVIEW 6 major objections 5 minor 66 references

The need for modern computing paradigm: Science applied to computing

T0 review · 6 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper argues that parallelized sequential computing has an inherent performance ceiling because the effective serial fraction grows with core count, and proposes an explicitly many-processor paradigm to break it.

desk verdict A visionary essay that restates Amdahl's law with fitted alphas and pushes the author's own EMPA architecture; the central claim that the serial fraction grows with core count is asserted, not derived, but the paper honestly labels its own figures as illustrative and at least raises a real problem worth arguing about. read the letter →

arxiv 1908.02651 v3 pith:7PIY4AKX submitted 2019-08-02 cs.GL

János Végh This is my paper · ORCID

classification cs.GL

keywords moderncomputingparadigmperformancewallAmdahl'sLawparallelizedsuperquasi-threadsEMPAefficiency

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

Drawing an analogy with the transition from classical to modern physics, the paper argues that today's computing paradigm is valid only in a 'normal' regime. At extreme scale—millions of aggregated processors—the non-parallelizable fraction of a task stops being a constant and grows with the number of cores, so Amdahl's Law turns from a scaling model into an absolute ceiling on payload performance. Measured HPL and HPCG efficiencies of recent supercomputers are presented as evidence of saturation, with performance declining as communication and synchronization overheads dominate. The paper concludes that exascale systems cannot be built by extending the classic architecture, and proposes the Explicitly Many-Processor Approach (EMPA), whose quasi-threads and hierarchic communication change the dependence on core count from linear to logarithmic. A sympathetic reader would care because the claim, if right, reclassifies the current speedup bottleneck as a law of nature rather than an engineering limitation.

What carries the argument

The load-bearing object is Amdahl's Law with a variable effective serial fraction, written in the paper as $\frac{R_{Max}}{R_{Peak}}=\frac{1}{N(1-\alpha)+\alpha}$, where $\alpha$ is the parallelizable payload fraction and $N$ the number of cores. The 'modern' twist is that $1-\alpha_{eff}$ is not a constant of the application: it grows with $N$ through contributions from operating-system scheduling, context switching, synchronization loops, propagation delay, and the single core that must orchestrate all others. The proposed remedy, EMPA, rests on the quasi-thread—an atomic unit that fuses one hardware core and one software fragment—plus hierarchic, direct core-to-core communication and hardware-assisted runtime mapping of virtual processors to physical cores. This machinery converts the per-core coordination overhead from a term that grows with $N$ into one that grows only logarithmically, which is what would let efficiency survive at exascale.

What would settle it

Take a scalable parallel application on a machine with millions of cores, and measure its effective serial fraction $1-\alpha_{eff}$ from Amdahl's Law as the core count rises while the per-core problem size is held constant. The paper predicts this fraction rises with $N$ and eventually pulls total performance down; observing instead that it stays flat or falls, with speedup continuing to track constant-$\alpha$ Amdahl scaling, would falsify the claimed ceiling.

Watch

Extended reading notes

Core claim

The paper's central claim is that parallelized sequential computing, as organized by the classic single-processor paradigm, has a finite 'speed of light': as the number of cores grows, the total payload performance $R_{Max}$ first tracks $N \times Perf_{single}$, then saturates and eventually declines, because the effective non-parallelizable fraction $1-\alpha_{eff}$ is itself an increasing function of $N$. This turns Amdahl's Law $\frac{R_{Max}}{R_{Peak}}=\frac{1}{N(1-\alpha)+\alpha}$ from a statement about algorithmic serial sections into a physical bound on the whole system, with contributions from operating-system overhead, context switching, propagation delay, and the 'first core must speak to all fellow cores' communication pattern. The paper reads the saturation visible in HPL and HPCG measurements of recent large supercomputers as evidence that this bound is already being approached, and it proposes EMPA as the 'modern' paradigm: processing capacity treated as a resource, quasi-threads (hardware core plus software fragment as one atomic unit), runtime mapping of virtual processors to physical cores, hardware-managed scheduling and synchronization, and hierarchic core-to-core communication that changes the dependence on core count from linear to logarithmic. If EMPA is right, exascale is reachable; if the Amdahl bound is unavoidable under the classic paradigm, exascale is not.

Load-bearing premise

The load-bearing premise is that the non-parallelizable fraction of a task is not a fixed algorithm property that shrinks with problem size, but grows with the number of cores and has a technology floor; if a large enough problem can keep that fraction constant, Amdahl's Law does not impose an absolute ceiling.

Editorial extensions

If this is right

Under the classic paradigm, adding cores beyond a few million does not increase payload performance; it eventually decreases it, so exascale targets cannot be met by scaling today's architecture.
Measured efficiency gaps between HPL and HPCG benchmarks reflect different communication intensities; the more communication a workload needs, the lower its 'speed of light' ceiling.
Processor-based full-scale brain simulation and large artificial-neural-network simulators will remain stuck orders of magnitude below their nominal capacity unless the paradigm changes, because their synchronization and communication needs collapse performance.
Feasibility studies for national exascale programs have been missing the existence of this inherent bound; a modern paradigm such as EMPA is required.
In EMPA, the compiler passes 'configware' meta-instructions to the processor, and the runtime maps quasi-threads to cores, so the architecture adapts to the task and removes most OS scheduling overhead.

Reading between the lines

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

If the paper's mechanism holds, the same ceiling should appear in any architecture with a single orchestrating core or shared synchronization point, not just the systems analyzed here; measuring $\alpha_{eff}$ on a large many-core chip at increasing core counts would test this directly.
The relativistic analogy suggests a dimensionless saturation constant for each interconnection technology; calibrating it on current clusters could turn the predicted wall into a design parameter for exascale planning.
An inference the paper does not draw: architectures that avoid global synchronization—dataflow, systolic, or neuromorphic designs—should also postpone the wall, making EMPA one instance of a broader design principle.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

You should know this paper is not a research result. It is a position essay built around Amdahl's law and the author's previous EMPA architecture, repeated from [11], [52], and the performance-wall papers [8], [34]. The only genuinely new framing is the physics analogy, and the authors themselves say it does not imply direct correspondence. The main formula in Table I is standard Amdahl's law with a relabeled parallel fraction; it reduces exactly when the lowercase n is read as cores. So a reader expecting a new quantitative law will not find one.

What the paper does well is to gather Top500 measurements and plot them against a simple Amdahl surface, which gives a useful visual reminder that supercomputer efficiency has been flattening for years. The discussion of why large-scale neural simulation hits a wall is also worth a look, even though the roofline there is explicitly guessed. The EMPA proposal is at least concrete enough to have a simulator, and the paper lists specific design choices (quasi-threads, configware, hierarchic buses) that could be tested.

The soft spots are real and central. The load-bearing premise is in Section II-A: the non-parallelizable fraction grows with core count and has a technology floor, making the parallel performance cap 'prohibited by the laws of nature.' That growth is asserted, not derived. Figure 6 says the numbers are 'purely illustrating the concepts,' and the brain-simulation curve is 'rather guessed.' The supporting citations are mostly the author's own prior work. The fitted alpha values in Figure 4 have no error budget and are not compared against a constant-alpha Amdahl baseline. The paper also conflates an implementation limit of current message-passing with a law of nature, while proposing a different implementation as the fix. Standard Amdahl scaling allows the serial fraction to shrink with problem size, as the paper itself notes from Singh, Hennessy and Gupta, and then simply asserts that at large core counts it starts to dominate. No crossing point or scaling model is given.

So the central argument does not hold as stated. The paper is honest about its own limitations, which is something, and it is not incoherent. But the missing derivation is exactly what would make the claim worth taking seriously.

Who is this for? Anyone interested in the 'performance wall' debate or in a readable summary of the EMPA manifesto would get something from the graphs and the references. It is not for someone seeking verified results.

My recommendation: this paper should go to peer review rather than be desk rejected, because the question is important and the author has at least shipped a simulator and a set of benchmark-derived plots. But a serious referee should demand a derived scaling law for the serial fraction with actual error bars, and a comparison against the standard constant-alpha Amdahl baseline. As is, it is a useful discussion piece, not a result.

Referee Report

6 major / 5 minor

Summary. This paper argues that parallelized sequential computing is approaching a fundamental efficiency limit analogous to the speed of light, and that standard scaling laws with a core-count-dependent serial fraction make exascale-class systems impossible under the 'classic' computing paradigm. It draws on analogies to special relativity, general relativity, quantum mechanics, and the uncertainty principle, and proposes a 'modern computing paradigm' called EMPA (Explicitly Many-Processor Approach) built around quasi-threads, hierarchical buses, configware, and hardware-supported scheduling. The central quantitative claims are that the non-parallelizable fraction grows with the number of cores, that performance is therefore absolutely capped, and that EMPA changes the dependence of performance on core count from linear to logarithmic.

Significance. If established, the paper would provide a unified explanation for the efficiency collapse of large supercomputers and would motivate a genuinely new architecture direction. The paper deserves credit for focusing on a real problem—communication and synchronization overhead destroying parallel efficiency—and for proposing concrete mechanisms (hierarchical buses, hardware-assisted thread management, direct core-to-core cooperation) rather than only criticizing the status quo. It also makes a simulator repository (EMPAthY86) available. However, the central quantitative claims are not established: the 'modern' formula reduces to standard Amdahl's law, the growth of the serial fraction with core count is asserted rather than derived, key figures are explicitly illustrative or 'guessed,' and the EMPA scaling claim is supported only by the authors' own prior references. The paper is better read as a vision statement than as a validated scientific contribution.

major comments (6)

[Table I, Section II-A] The 'modern' performance formula Perftotal(N) = N·Perfsingle/(n·(1−α)+α) reduces to standard Amdahl's law when the lowercase n is interpreted as the number of cores N (giving Perftotal = Perfsingle/((1−α)+α/N)). The claimed new correction is therefore not a new law but a restatement of Amdahl's law; the paper should acknowledge this and then separately derive the additional N-dependence it claims for (1−α).
[Section II-A, Figure 6] The load-bearing premise that the non-parallelizable fraction (1−α_eff) increases with the number of cores and has a technology floor is asserted without derivation. Figure 6's own caption states that it is 'purely illustrating the concepts; the displayed numbers are somewhat similar to the real ones,' and no equation, crossover condition, or measurement is given for the growth of (1−α_eff). Without such support, the claim that performance is 'prohibited by the laws of nature' is not justified.
[Section II-C] The paper quotes [25] as saying that the serial fraction is a diminishing function of problem size, then asserts that at sufficiently large core counts it starts to dominate. No scaling model or data is provided to reconcile these two statements or to locate the crossover, leaving a direct gap in the core argument.
[Section II-A, Figure 4] The α_eff values used to draw the HPL and HPCG rooflines are fitted to the same TOP15 measurements they are used to explain, and the paper reports no error budget and no comparison against a constant-α Amdahl baseline. The brain-simulation roofline is explicitly 'rather guessed' [35]. These figures therefore do not provide independent evidence for the predicted saturation.
[Section II-A vs. Section II introduction] The paper asserts that exceeding a certain computing performance 'is prohibited by the laws of nature,' but it also states that 'unlike in the nature, the technical implementation of the critical points can be changed.' Since communication overhead is an implementation property (as the EMPA discussion itself argues), the claimed fundamental limit is internally inconsistent as stated and should be reframed as a limit of current implementations.
[Section III-C] The central positive claim that EMPA changes the dependence on the number of cores 'from linear to logarithmic [8]' and thereby enables exascale systems is not supported by simulation results, measurements, or an independent derivation; reference [8] is the authors' own prior work. The manuscript needs at least a quantitative scaling model or simulator output before this claim can be evaluated.

minor comments (5)

[Throughout] The manuscript contains numerous typographical and grammatical errors (e.g., 'consitutes', 'the theory was almost forgotten', inconsistent use of 'efficacy' and 'efficiency') that should be corrected in any revision.
[Table I] The lowercase n in the computing formula is never defined; its physical meaning (if any) and its relationship to N should be clarified.
[Figure 6] The axis labels and legend mix α and (1−α); e.g., the left scale is labeled (1−α^X_eff) while the internal curves are named α_SW, α_OS, α_eff, which is confusing and should be made consistent.
[Figure 3] The figure plots RMax over time without error bars or source-level data for individual points; the anecdotal discussion of Gyoukou ('mystic') should be either supported by data or removed.
[References] Load-bearing claims rely heavily on the authors' own prior or under-review papers ([8], [34], [35], [40]); independent references or direct evidence should be supplied.

Circularity Check

3 steps flagged · score 6.0 of 10

Central nature-imposed performance ceiling rests on a self-cited growing serial fraction, and the plotted Amdahl curves use alpha values fitted from the same data they are used to explain.

self citation load bearing [Section II-B, paragraph on 'dark performance']
"Due to the classic computing principles, the first core must speak to all fellow cores, and this non-parallelizable fraction of the time increases with the number of the cores[34], [40]."

This sentence supplies the decisive premise that turns standard Amdahl's law into an absolute nature-imposed limit: if (1-alpha_eff) grows with N, then RMax saturates and eventually decreases. The cited support, [34] and [40], are both authored by the present first author; [34] is elsewhere in the paper described as 'in review'. No independent derivation, measurement, or external theorem is provided for the core-count dependence of the serial fraction. Later claims that 'this feature alone prevents building exascale supercomputers [8]' and that EMPA changes the dependence 'from linear to logarithmic [8]' inherit the same self-citation chain, so the central argument reduces to the author's prior assertion rather than to an independently checkable result.
fitted input called prediction [Section II-A, Figure 4 caption and surrounding text]
"The diagram lines marked as HPL and HPCG correspond to the behavior of supercomputer Taihulight at (1−αeff) values 3.3∗ 10−8 and 2.4∗ 10−5, respectively."

The (1-alpha_eff) values used to draw the Amdahl-law curves in Figure 4 are not predicted from any model; they are inferred from Taihulight's measured RMax/RPeak and core count, i.e. from exactly the kind of efficiency points the curves are then used to 'explain' and to demonstrate saturation. Because the Amdahl formula is monotone in (1-alpha), every measured point can be assigned a value of this single free parameter that places it on the family of curves, so the agreement is tautological. No error budget, no independent dataset, and no constant-alpha baseline is given. The 'saturation effect' is therefore a fitted restatement of the data rather than an independently derived prediction.

1 more flagged steps

self definitional [Figure 6 caption, Section II-B]
"The figure is purely illustrating the concepts; the displayed numbers are somewhat similar to the real ones."

Figure 6 is the only in-paper 'demonstration' of the critical inflection and the claim that 'at some critical value where an inflection point occurs, the resulting performance starts to decrease.' But the caption admits the figure is purely illustrative, with numbers chosen by the authors rather than derived from measurements or from Amdahl's law. The alpha_SW, alpha_OS, and alpha_eff curves are inputs, and the RMax curve is then computed from those chosen inputs. The predicted performance breakdown is therefore built into the construction: the conclusion is equivalent to the assumed shape of the illustrative curves, not an independent result.

full rationale

Amdahl's law and the existence of some serial fraction are standard external inputs, and the TOP500 data in Figure 4 are real measurements; the EMPA proposal itself is an independent design idea rather than a circular derivation. The circularity is concentrated in the extra, decisive step: the claim that (1-alpha_eff) grows with the number of cores and has a technology floor is not derived in this paper, but is assigned to the author's own prior works [8], [34], [35], [40]; the only in-paper visual support, Figure 6, is explicitly labeled illustrative. Additionally, the Figure 4 curves are drawn using alpha values read off Taihulight's measured efficiency, so they cannot independently verify the saturation they display. Thus the paper's quantitative core prediction, that classic-paradigm exascale is prohibited by the laws of nature, reduces to a fitted parameter and a self-citation chain. A score of 6 reflects partial circularity: the external Amdahl framework and real benchmark data keep the paper from being wholly circular, but the central 'limit' claim is not independently established.

Assumptions & free parameters 3 free parameters · 5 assumptions · 2 invented entities

The central efficiency equation is standard Amdahl's law. The paper's added content is a set of assumptions about growing serial fractions, physics analogies, and benchmark representativeness, plus proposed entities without independent validation.

free parameters (3)

alpha (parallel fraction) for Amdahl examples = 1 - 1e-3 (1993 example); 1 - 3.3e-8 (Taihulight HPL); 1 - 2.4e-5 (Taihulight HPCG)
These values are assigned to reproduce observed efficiencies of specific systems. They are not derived from first principles, and they drive the central efficiency formula.
non-parallelizable fraction for brain simulation roofline = Roughly 1e-4 to 1e-3, called 'rather guessed' in the text
The dashed brain-simulation saturation curve in Figure 6 is guessed rather than measured, as the paper itself states.
illustrative single-processor performance P = 1 Gflop/s @ 1 GHz
Figure 6 uses a fictional supercomputer with this performance and the caption says the figure is purely illustrative.

assumptions (5)

standard math Amdahl's law describes the performance of parallelized sequential systems.
Used throughout Section II and Table I as the base formula for efficiency. It is a standard textbook result, not derived in the paper.
ad hoc to paper The non-parallelizable fraction grows with the number of cores and has a technology-given floor.
This is the load-bearing premise behind the performance wall and the laws-of-nature conclusion. The paper supports it with the author's own references [8], [34] rather than an independent derivation.
ad hoc to paper Relativistic and quantum physics analogies are valid guides for computing behavior.
The paper says the analogies do not imply direct correspondence, yet it uses them to claim qualitative new phenomena and a nature-imposed limit, so the transfer of concepts is an assumption.
domain assumption TOP500 HPL and HPCG measurements represent payload performance.
Figure 4 and the efficiency discussion treat these benchmarks as the measure of real useful performance, with no independent verification.
domain assumption The 1 ms biological grid time in brain simulation acts as a fixed synchronization quantum.
Used in Section II-C to argue that brain simulation has a quantal time limit; this is taken from prior brain-simulation practice [35], [39].

invented entities (2)

quasi-thread (QT)
purpose: Atomic unit combining a hardware core and a software thread, giving processing capacity a dual hardware/software nature that is mapped to physical cores at runtime.
Introduced in Section III-B as the core idea of EMPA. The paper mentions an early simple simulator but provides no measurement demonstrating QT behavior or benefit.
configware
purpose: Compiler-embedded meta-instructions that let the processor adapt its configuration to runtime resource availability.
Part of the EMPA proposal in Section III-B; no implementation or benchmark is shown in the paper.

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Cite this review

Pith. "Pith review of The need for modern computing paradigm: Science applied to computing." pith.science (2026). https://pith.science/paper/7PIY4AKX

@misc{pith2026190802651,
  author       = {Pith},
  title        = {Pith review of: The need for modern computing paradigm: Science applied to computing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7PIY4AKX}},
  note         = {Machine review of arXiv:1908.02651}
}

read the original abstract

More than hundred years ago the 'classic physics' was it in its full power, with just a few unexplained phenomena; which however led to a revolution and the development of the 'modern physics'. Today the computing is in a similar position: computing is a sound success story, with exponentially growing utilization, but with a growing number of difficulties and unexpected issues as moving towards extreme utilization conditions. In physics studying the nature under extreme conditions has lead to the understanding of the relativistic and quantal behavior. Quite similarly in computing some phenomena, acquired in connection with extreme (computing) conditions, cannot be understood based on of the 'classic computing paradigm'. The paper draws the attention that under extreme conditions qualitatively different behaviors may be encountered in both physics and computing, and pinpointing that certain, formerly unnoticed or neglected aspects enable to explain new phenomena as well as to enhance computing features. Moreover, an idea of modern computing paradigm implementation is proposed.

Figures

Figures reproduced from arXiv: 1908.02651 by the authors.

**Figure 2.** A general model of parallel operation. For better visibility, the lengths of the boxes are not proportional with the time the corresponding action needs. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The RMax payload performance in function of the year of construction for different configurations. The performances of all configurations seem to have their individual saturation value. The later a supercomputer appears in the competition, the smaller is the performance ratio with respect to its predecessor; the higher is its rank, the harder is to improve its performance. of the HPCG level. Supercomputers are comp… view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: How the speed of a body accelerated by g depends on the time, in relativistic approach, see also table I. Compare to Figs. 4 and 6 to the finite propagation of the physical fields, and even the latency time of the interfaces can be paired with creating and attenuating …

**Figure 6.** Figure 6: B the moderately increased one (corresponding to [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

Discussion (0). Continue with ORCID to comment.

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