Solutions

Optimization On-Demand, Your Way

Solutions for every workflow and pipeline.

TALPification
as a Service

Beta by Invite

Highly-Available · Maintenance Free · On-Demand

Have code that needs to be TALP parallelized and optimized today? Get started immediately by connecting your repositories and getting on-demand optimization. All you need is 5-minutes to sign-up, get connected and witness the awesome speedup of TALPs.

Self-Hosted
TALPification

Coming Soon

Sovereign & Secure · Privately Parallelize

Keep your code and workflows managed entirely in-house. Massively Parallel's TALP engine will be deployed on your servers, behind your firewalls. Your code never leaves your environment.

Enterprise Services

By Request

Execute on saving time and energy today.

Work directly with Massively Parallel engineers. Together we will identify the areas of your organization's applications and algorithms that benefit most from TALP parallelization and optimization.

Automatic On-Deploy
TALPification

On Roadmap

JIT Architecture-Targeted Parallelization & Optimization

Make optimization part of your deployment process. Integrate Massively Parallel's TALP engine with your deployment tool pipeline. Code will be TALP parallelized and optimized for the target architecture's execution environment based on pre-configured cost, energy and speedup goals.

IDE-Based
TALPification

On Roadmap

Uninterrupted: Parallelize Where You Code

Don't ever leave your code. Our in-IDE solutions allow for you to make TALPs a part of your daily workflow. No new tools to learn. No new integrations into your dev toolchain. Just a simple extension and your same algorithms are better, faster and more efficient.

TALP MCP Server

On Roadmap

Coding AgentsMPT's TALPs MCP Server

Connect AI applications and agents directly to MPT's Model Context Protocol (MCP), enabling agents to access all our TALP Engine's analysis and optimization tools, expediting AI's ability to make TALPs a part of AI workflows and code generation.

Solutions

Multiple paths to the same outcome: faster software, lower energy use, and more intelligent execution.

TALPs are not a single packaging decision. They are a capability that can be delivered in different ways depending on how a team builds, deploys, secures, and operates software. This page is about the business cases: how TALPs show up in the real world.

Core idea

Same code. Same logic. Different execution in time.

TALPification reshapes when work executes, how it is scheduled, and how it aligns with the target environment. That makes TALPs deployable as a service, an internal platform, a pipeline step, an IDE workflow, or an AI-facing capability.

Delivery Models

Pick the operating model that fits the business.

Different organizations adopt infrastructure differently. The right TALP solution depends on control requirements, internal workflow maturity, deployment strategy, and where the customer wants the value to appear.

Beta by Invite

TALPification as a Service

Delivery Model

Highly available, maintenance-free, on-demand optimization.

For teams that want the fastest path to value, a hosted TALP service makes it possible to connect a repository, analyze execution pathways, optimize for speed or energy, and return TALPified code plus reports without standing up internal infrastructure.

Best for teams that want to validate value quickly
Low operational overhead and fast onboarding
Useful for pilot projects, targeted applications, and external-facing systems
Ideal when speed to insight matters more than infrastructure control

Coming Soon

Self-Hosted TALPification

Delivery Model

Sovereign, secure, privately parallelize inside your environment.

For organizations with sensitive IP, regulated workflows, or strict internal controls, the TALP engine can run inside customer-managed infrastructure so code, analysis, and outputs remain behind internal security boundaries.

Best for regulated, defense, enterprise, or proprietary environments
Keeps code and workflow management entirely in-house
Supports internal governance, audit, and deployment standards
Designed for teams that need the same capability with stronger operational control

By Request

Enterprise Services

Delivery Model

Work directly with MPT on the highest-value opportunities.

Some organizations want more than tooling. They want expert guidance on where TALPs will matter most first. Enterprise services are for customers who want MPT involved in identifying priority code paths, measuring opportunity, and accelerating adoption.

Best for large organizations with multiple candidate systems
Useful when prioritization matters as much as implementation
Supports internal champions with technical and strategic guidance
Focused on shortening time-to-value in high-impact environments

On Roadmap

Automatic On-Deploy TALPification

Delivery Model

Optimization built into deployment workflows.

This model makes TALPification part of CI/CD and deployment. Code is optimized for the target execution environment based on predefined speed, cost, or energy goals so optimization becomes part of software delivery rather than a separate event.

Best for platform teams and repeatable deployment pipelines
Aligns code optimization with runtime and architecture targets
Supports architecture-specific delivery strategies
Turns TALPification into an operational capability, not a one-off project

On Roadmap

IDE-Based TALPification

Delivery Model

Parallelize where developers already work.

This delivery path brings TALP analysis and optimization into the editor so developers can inspect, preview, and apply TALPified changes without leaving their normal workflow.

Best for developer adoption and faster iteration loops
No new environment to learn for day-to-day use
Supports preview, diff review, and safe application of changes
Helps make TALPs part of normal software development, not a separate specialty

On Roadmap

TALP MCP Server

Delivery Model

Expose TALP analysis to AI agents and code-generation workflows.

As AI coding agents become a larger part of software delivery, TALP capabilities can be exposed through MCP so agents can query analysis, retrieve execution-pathway context, and incorporate TALP-aware optimization into software generation and review.

Best for AI-native software workflows
Lets agents access TALP analysis and optimization tools directly
Supports future code-generation pipelines that are performance-aware by default
Creates a business case for TALPs inside emerging AI engineering stacks

TALPification as a workflow

Every delivery model follows the same core motion.

Whether TALPs are delivered through a hosted service, a self-hosted deployment, an IDE extension, or an MCP endpoint, the underlying story is consistent: take existing code, analyze execution pathways, choose an objective, target the runtime environment, and return an improved outcome.

Input

Bring in existing code

A repository, local project, or internal source system becomes the source of truth. TALPs are designed to work with real software, not toy examples built for demos.

Analysis

Find the execution pathways that actually drive runtime

The TALP engine performs whole-program analysis, identifies Time-Affecting Linear Pathways, and separates the work that stays fixed from the work that changes with the input.

Objective Selection

Tune for speed, energy, or a balanced objective

The same software can be optimized toward different operational outcomes depending on the business need: lower latency, lower energy consumption, or a balance between the two.

Targeting

Aim the result at the real deployment environment

Optimized outputs can be tuned for target architectures and execution environments so TALPification is not abstract optimization. It is environment-aware delivery.

Outputs

Return TALPified code and guidance

The result is not just transformed code. It is code plus reports, expected outcomes, and deployment metadata that support adoption and operational decision-making.

Business cases

This page should answer: “How does this become a real product?”

The answer is not one product. It is multiple commercially and operationally meaningful paths for delivering TALP value to different kinds of organizations.

Example business cases

Existing software modernization without rewriting core algorithms
AI systems that need better throughput per watt
Data-center environments constrained by energy, cooling, or cost
Edge and embedded systems operating under strict thermal or battery limits
Platform teams that want optimization integrated into deployment workflows
Engineering organizations adopting AI coding agents and agentic tooling

Closing position

One technology. Multiple adoption paths.

The Solutions page should make it obvious that TALPs can be bought, deployed, and used in more than one way. The value is consistent. The delivery model flexes to fit the customer.

Services: Work with MPT to Optimize and Parallelize Your Code

TALPification as a Service

Highly available, maintenance-free, on-demand optimization.

Best for teams that want to validate value quickly
Low operational overhead and fast onboarding
Useful for pilot projects, targeted applications, and external-facing systems
Ideal when speed to insight matters more than infrastructure control

Input

GitHub Repository

Private or public repo

Read-only access (service pulls code)

Customer-controlled source of truth

Code Never Locked In

TALPification Engine

Secure Cloud TALPification Engine

Whole-program analysis — identifies Time-Affecting Linear Pathways — rewrites execution in time

A) Ingestion & Parsing

Pull code, parse, build whole-program representation

B) TALP Analysis Core

Find time pathways & overlap potential

Not just task splitting — time restructuring

C) Optimization Objective Selector

SpeedEnergyBalanced

D) Architecture Targeting

x86 / ARM / RISC-V — single-node / cluster / cloud configs

Produces optimized code tailored to deployment environment

x86ARMRISC-VCloud/HPC

Outputs

TALPified Source Code

Same logic

Time-restructured execution

Performance & Energy Report

Expected speedup

Power/cooling impact

Deployment Metadata

Target arch + config

CI/CD-ready outputs

Same Code — New Time Flow

Self-Hosted TALPification

Sovereign, secure, privately parallelize inside your environment.

Best for regulated, defense, enterprise, or proprietary environments
Keeps code and workflow management entirely in-house
Supports internal governance, audit, and deployment standards
Designed for teams that need the same capability with stronger operational control

Customer Infrastructure

On-Prem / Private Cloud

Corporate data center

Private cloud (VPC)

Air-gapped / regulated networks

Internal Git Server

GitHub Enterprise / GitLab / Bitbucket

CI / CD Pipeline

Build • Test • Deploy

Secure Compute Cluster

HPC • Servers • Private Cloud

Code Never Leaves Your Network

Self-Hosted Engine

MPT TALPification Engine (Self-Hosted)

Identical TALP logic as cloud service, deployed locally

1) Local Ingestion & Parsing

Pulls from internal repos only

2) TALP Analysis Core

Discovers Time-Affecting Linear Pathways

Rewrites execution order in time

3) Policy & Objective Controls

SpeedEnergyDeterministic

4) Architecture Targeting

Optimized for local hardware & schedulers

Outputs

TALPified Source Code

Stored in internal repos

Performance / Energy Reports

Internal visibility only

CI / Runtime Integration

Used by schedulers & build systems

Full IP & Data Sovereignty

Enterprise Services

Work directly with MPT on the highest-value opportunities.

Best for large organizations with multiple candidate systems
Useful when prioritization matters as much as implementation
Supports internal champions with technical and strategic guidance
Focused on shortening time-to-value in high-impact environments

Automatic On-Deploy TALPification

Optimization built into deployment workflows.

Best for platform teams and repeatable deployment pipelines
Aligns code optimization with runtime and architecture targets
Supports architecture-specific delivery strategies
Turns TALPification into an operational capability, not a one-off project

Job Submission

Code + Run Intent

Submitted to cloud / data center / HPC queue

submit_job(

src="repo@commit",

goal="balanced",

max_cost="$",

)

Per-Run Optimization Request

Constraints

Deadline / SLA

Cost ceiling

Energy or carbon budget

Preferred architectures

Runtime TALPification Service

TALPifies for the specific execution environment and current operational conditions

1) Environment Discovery

Query available resources: CPU types, nodes, accelerators

Read current pricing / power / scheduling constraints

2) Objective + Constraint Solver

FastestLowest EnergyLowest Cost

3) TALP Analysis + Rewrite (Per Run)

Find time pathways, reshape execution timing for target environment

Generates run-specific TALPified artifacts

4) Package + Dispatch

Deliver optimized code/binary to the selected runtime pool

Near-real-time TALPification at submit-time

Execution

Selected Execution Pool

Runs TALPified artifacts on best-fit resources

Pool A: Fastest Nodes

High performance • higher cost

Pool B: Energy-Optimized

Lower power • lower thermal load

Pool C: Lowest Cost

Spot / off-peak • budget-first

Optimized For This Run

IDE-Based TALPification

Parallelize where developers already work.

This delivery path brings TALP analysis and optimization into the editor so developers can inspect, preview, and apply TALPified changes without leaving their normal workflow.

Best for developer adoption and faster iteration loops
No new environment to learn for day-to-day use
Supports preview, diff review, and safe application of changes
Helps make TALPs part of normal software development, not a separate specialty

Developer

IDE Workspace

Local project or repo checkout

Works on a branch / PR workflow

Developer controls changes

main.c

for (i=0; i<n; i++) {

step(a[i]);

}

Run TALPification In IDE

IDE Plugin

MPT IDE Extension

Analyze your code, choose objectives, preview changes, and apply TALPified updates

A) Local Analysis & Context

Understands project structure and dependencies

B) TALP Analysis Core

Find time pathways & overlap potential

Restructure execution timing (not task splitting)

C) Objective Selector

SpeedEnergyBalanced

D) Preview + Apply

Diff view, annotations, and one-click apply

Commit TALPified changes to a branch or PR

Outputs

TALPified Code Changes

IDE diff + annotations

Applied to branch / PR

Performance & Energy Report

Expected speedup

Power/cooling impact

Target Profiles

Laptop / workstation / server

x86 / ARM / cloud configs

Stays In Your Dev Workflow

TALP MCP Server

Expose TALP analysis to AI agents and code-generation workflows.

Best for AI-native software workflows
Lets agents access TALP analysis and optimization tools directly
Supports future code-generation pipelines that are performance-aware by default
Creates a business case for TALPs inside emerging AI engineering stacks