Hemant Ghatawal’s Post

Enzyme performs automatic differentiation (AD) for Julia by using LLVM compilation. That enforces quite disciplined programming. Or Enzyme compiles all your project. In huge and complex neural networks projects that becomes quite the problem. Julia currently cannot compile all program like Golang does. Julia uses JIT compilation. HPC on pinned CPU cores is one nasty topic too. Add in here Enzyme, and you have a problem. Scientists are not experienced programmers. They will fall in the trap for sure. After few years I will release some source code. Currently it is about forking Julia a little as well. Its multi threading needs HPC modification.

🚀 From Software Developer to Quantum Developer — A Journey Into the Future ⚛️ Every great transformation begins with curiosity. Imagine a developer - let’s call them Alex—who starts asking a simple question: “What is Quantum Computing?” That question sparks a journey that leads to the frontier of technology. 💡 Here’s how that transformation unfolds: 🔹 1. Build on Your Software Foundation Your existing skills in programming, algorithms, and problem-solving are your biggest advantage. Languages like Python, C++, and Java already set the stage. 🔹 2. Learn Quantum Fundamentals Dive into the basics of quantum mechanics - qubits, superposition, entanglement, and measurement. This is where classical thinking begins to evolve. 🔹 3. Get Hands-On with Quantum Tools Start experimenting with platforms like IBM Quantum, Qiskit, Cirq, or Q#. Run simulations and explore real quantum hardware. 🔹 4. Build Real Projects Apply your knowledge by working on quantum algorithms like Grover’s or Shor’s. Explore domains like optimization, cryptography, and AI. 🔹 5. Become a Quantum Developer Contribute to open-source projects, join communities, and stay updated in this fast-growing field. Continuous learning is the key. 🌱 Mindset Matters: Stay curious. Embrace complexity. Collaborate. And most importantly—keep learning. 🌍 The shift from classical to quantum is more than a career move—it’s stepping into the future of computation. ✨ From writing code for today’s computers to building solutions for tomorrow’s quantum machines #QuantumComputing #SoftwareDevelopment #CareerGrowth #FutureTech #Innovation #Learning #QuantumDeveloper #Technology #AI #DigitalTransformation

Quantum computing is closer to your daily stack than you think. By 2030, it'll be as expected as knowing Git. Here's where to start 👇 ⚛️ 3 concepts that actually matter: - Superposition - qubit holds 0 and 1 at the same time - Entanglement - two qubits linked; change one, the other reacts instantly - Interference - amplify right answers, kill wrong ones 🛠️ Pick your framework: - Qiskit - easiest entry, huge community, Python - PennyLane - if you're into AI/ML - Q# - Microsoft ecosystem 🖥️ Where to practice for free: - IBM Quantum Experience - Azure Quantum - Google Cirq The devs learning this in 2026 will architect what everyone else builds in 2030. 💬 Already exploring quantum? Drop your experience in the comments. Would love to see where the community is at.

The 2026 Shift – Beyond Binary! Why "Classical Computing" is no longer enough for the AI Revolution! We are witnessing a monumental shift in the IT landscape. As we scale our AI models, the energy consumption and processing limits of traditional silicon are hitting a wall. The solution? Hybrid Quantum-Classical Architecture. As a PhD Scholar and Software Engineer, I’ve been tracking how Python is evolving to become the primary interface for Quantum libraries. We are no longer just processing data; we are simulating possibilities at an atomic level. Why 10,000+ Professionals should care about this TODAY: Efficiency: Hybrid systems can train LLMs in 40% less time with 60% less energy. Security: Post-Quantum Cryptography (PQC) is becoming mandatory for Cybersecurity consulting. The Python Factor: Libraries like Qiskit and PennyLane are making Quantum programming accessible to every Python developer. My Take as a Professor: Our curriculum must change. We shouldn't just teach "Data Structures"; we must teach "Quantum-Ready Data Structures." The bridge between academia and the $100B Quantum industry is where the next million jobs will be created. Let’s Build a 10K+ Strong Tech Community! I am looking to connect with Tech Visionaries, IT Directors, and Research Scholars who believe in a sustainable and faster digital future. Are you ready for the Quantum Leap, or do you think we are still years away? Let's debate in the comments! #QuantumComputing #GreenAI #PythonProgramming #TechInnovation2026 #FutureOfIT #ProfessorDhimesh #PhDLife #SoftwareEngineering #ITConsulting #ScalableTech

I had a question that kept bothering me: How does a AI model actually become instructions on a chip? Not theoretically. Not "the compiler handles it." I wanted to understand every layer myself. So I built the stack from scratch. Project 1 - FX2Accel: I started at the top. FX2Accel captures a PyTorch model using toch.fx, propagates these tensor shapes through every node in the graph, fuses operations like Linear+ReLU into single kernels, eliminates dead code (dead code elimination), and plans memory so buffers get reused instead of wasting on-chip SRAM. The NumPy backend validates correctness at 0.0 difference vs PyTorch before anything touches the real hardware. Project 2 - KernelForge (Kernel Compiler) Once you know "what" to compute, you need to know "how" to execute it on a GPU. KernelForge takes fused ops, applies loop tiling (32x32 blocks) to keep data in fast shared memory reducing intermediate memory trips and emits CUDA style kernels where each thread gets its own index into the output. This project was inspired by how Triton approaches GPU programming. Project 3 - AccelSim (Hardware Simulator) The final layer. What does hardware actually do with the compiled instructions? AccelSim runs a cycle accurate simulation of a five instruction LOAD / MATMUL / RELU / STORE / ADD stream, tracks memory traffic, measures compute vs memory utilization, and identifies bottelnecks. A 2-layer MLP comes out compute bound at 77.8% - exactly the kind of signal that tells a compiler where to spend its optimization budget. Together these three projects form a complete feedback loop: 1. compile the graph 2. optimize the kernels 3. simulate the hardware 4. feed insights back up to readjust optimization budgets. It is a miniture version of what TVM, Triton, and real accelerator SDKs do at scale. I am always looking to explore oppurtunities in ML systems, compiler engineering, or accelerator hardware. If you are working on this stack and want to talk, I would love to connect Github link to the three projects: https://lnkd.in/g4wzAUyx

Join us today at the Computer Lab. William Moses will talk about "Making Waves in the Cloud: A Paradigm Shift for Scientific Computing through Compiler Technology" today (Apr 21st) at 4pm. Scientific models are today limited by compute resources, forcing approximations driven by feasibility rather than theory. They consequently miss important physical processes and decision-relevant regional details. Advances in AI-driven supercomputing — specialized tensor accelerators, AI compiler stacks, and novel distributed systems — offer unprecedented computational power. Yet, scientific applications such as ocean models, often written in Fortran, C++, or Julia and built for traditional HPC, remain largely incompatible with these technologies. This gap hampers performance portability and isolates scientific computing from rapid cloud-based innovation for AI workloads. In this talk, we bridge that gap by transpiling existing programs using the MLIR compiler infrastructure. This process enables advanced optimizations, deployment on AI hardware, and automatic differentiation. In particular, we demonstrate execution of a state of the art Julia-based ocean model (Oceananigans), with >277 custom single-node CUDA kernels on thousands of distributed GPUs and Google TPUs. Our results demonstrate that cloud-based hardware and software designed for AI workloads can significantly accelerate simulations, opening a path for scientific programs to benefit from cutting-edge computational advances. https://lnkd.in/eG5x8QFk

Quantum hardware is accelerating — but middleware is becoming the bottleneck. Many startups building quantum systems are constrained not by physics, but by a lack of engineering bandwidth to design robust middleware layers. At the same time, AI agents already have a de facto interaction standard: the Model Context Protocol (MCP). MCP isn’t magic. Under the hood, it’s simply structured communication over familiar RESTful APIs — which means it can be implemented as a lightweight Python service, co-located with quantum hardware control systems. This creates a pragmatic opportunity: → Expose quantum capabilities via MCP → Enable immediate compatibility with AI agent ecosystems → Skip building custom orchestration layers from scratch The result: out-of-the-box integration with agentic workflows, faster iteration cycles, and reduced middleware complexity. The quantum stack doesn’t need more abstraction — it needs better interfaces. #quantum #software #computing #ai

Algorithms I stated in my previous post, that i have been on a learning spree, learning about the system behind every models, programs and applications. This has lead me to study more about ALGORITHMS, and here a few things i have been able to pick up. Algorithm is simply the set of instructions that guides the execution of a task or program. There are certain features of an algorithm that determines the efficiency of the algorithm. - Clear Input: An algorithm is expected to have well defined and clear inputs - Clear Output: An algorithm must have well defined outputs, which also means the result of every program guided by an efficient algorithm must be clear, precise and unambiguous, which brings me to another feature of an algorithm. - Simple and Well defined instructions: an algorithm must not be unnecessarily complex, an efficient algorithm is not determined by the quantity of instructions, but the scalability of the instructions irrespective of the input size. Feasible: an efficient algorithm has to be simple and practicable. Finite-ness: an algorithm has to have an exit point, it must not result in infinite loop Language Independent: This is a beautiful feature of an algorithm, that allows one algorithm, work for multiple programming languages, ensuring flexibility and scalability. The knowledge i appreciate the most here is the Algorithm analysis, Types of Algorithm analysis and Algorithm Complexity, i found these so profound because it helped me understand the basics of developing a system that works on multiple fronts. I would be sharing more on this in my next post. #Startech #SoftwareDeveloper #SoftwareEngineer

🚀 Qiskit vs Cirq vs Q# – Which Quantum Framework Should You Learn? Quantum computing is no longer the future—it’s happening NOW. 💡 If you're stepping into this revolutionary field, choosing the right framework can shape your entire learning journey. Here’s a quick breakdown 👇 🔹 IBM’s Qiskit Perfect for beginners & researchers ✔ Python-based ✔ Huge community & tutorials ✔ Access to real quantum hardware 🔹 Google’s Cirq Best for NISQ-era experiments ✔ Fine-grained circuit control ✔ Hardware-focused optimization ✔ Ideal for advanced experimentation 🔹 Microsoft’s Q# Great for algorithm design & simulation ✔ Strong integration with .NET ✔ Azure Quantum ecosystem ✔ Advanced quantum programming 💥 So, which one should YOU choose? 👉 Beginner? Start with Qiskit 👉 Research & optimization? Go with Cirq 👉 Enterprise & algorithm design? Explore Q# 📈 The demand for quantum developers is rising rapidly. This is your chance to stay ahead of the curve and build future-ready skills. 🔥 Want to Learn Quantum Computing? At Coding Masters, we provide hands-on training in Quantum Computing with real-world projects and expert guidance. 📞 If you're interested, contact us today and start your journey into the future of technology! 💬 Which framework are you planning to learn? Comment below! #QuantumComputing #Qiskit #Cirq #QSharp #FutureTech #EmergingTech #AI #MachineLearning #TechCareers #Programming #Developers #Innovation #QuantumAlgorithms #TechLearning #CareerGrowth #Upskill #DigitalTransformation #CodingMasters

GPU kernels are hard to optimise. The dimensions are coupled, the search space is combinatorial, and the objectives compete with each other. Finding a breakthrough implementation takes real creativity. LLMs make evolutionary approaches practical here. Instead of random structural edits you get a mutation operator that understands code semantics and produces valid programs. And crucially, you get multiple ways to control diversity. Temperature and prompts control how radical each mutation is. You can vary selection pressure, share or isolate insights between candidates, and give each candidate a unique or shared plan. So we can explore broadly early, exploit the best candidates later. There are theoretical results for simple evolutionary problems that tell you exactly how fast selection kills diversity and where mutation rate tips from polynomial to exponential runtime. These problems are much simpler than kernel optimisation, but they provide useful heuristics for tuning these knobs in practice. This is what we're building at Geometric. We use evolutionary algorithms with LLM mutation operators to optimise GPU kernels, with diversity management guided by theory to control the search. Full post here: https://lnkd.in/e4-4hkKG