Imagination Technologies’ Post

There are many Parallel Communication Patterns in CUDA and one of them is the MAP MAP(ELEMENTS, FUNCTION) - set of elements to process - function to run on each element For example, squaring 64 floats GPUs are good at MAP because - GPUs have many parallel processes - GPUs optimize for throughput So as a GPU programmer, you are more interested in optimizing the time in which the entire MAP operation can be completed instead of optimizing the time for any element to complete MAP communication pattern is straightforward 1 element in --- 1 element out For example, we can solve the "addition of 1 to each element in the array" using the MAP function There are other communication patterns like Scatter, Gather, Reduce, Scan, etc #cuda #parallelprogramming #deeplearning

What is the real difference between CPUs and GPUs? Both components are essential for running computers. CPUs are the computer’s brain, and they perform all the general computer tasks; on the other hand, GPUs are a type of Application-Specific Integrated Circuits which perform specific tasks like 3D rendering through smaller but more cores enabling it to perform concurrent [parallel] calculations. Which one does AI utilize? Mainly, AI training utilizes GPUs since their parallel processing architecture and hardware optimizations significantly accelerate the process compared to CPUs because they split the required tasks amongst the multiple smaller cores to perform simultaneous computations. The video showcases the difference as explained by NVIDIA. Follow AI Nexus - Club for more….

Distributed training on multiple GPUs using NCCL and PyTorch NCCL is the standard communication backend for NVIDIA GPUs. we use NCCL for executing operations like all-reduce. NCCL works on a single or multiple machines and can use high performance networks as well. 1️⃣ similar to training on multiple CPUs, to train on multiple GPUs we need to initialize communication groups with `nccl` as the backend. `dist.init_process_group(backend="nccl")` 2️⃣ we need to make sure that each process is allocated to one GPU. to do this we can use `RANK` and assign it to `device` variable. 3️⃣ we can then use `torchrun` to launch distributed training. this way we can easily make our CUDA based programs run on multiple GPUs. #pytorch #deeplearning #distributedsystems

Training on multiple GPUs using NCCL and PyTorch

Vectorisation refers to the process of performing operations on entire arrays or matrices at once, rather than iterating over individual elements. Implementation of #machinelearning algorithms using vectorisation offers several benefits that include: -> Parallelisation: Modern CPUs and GPUs are optimised for performing operations on large arrays in parallel. Vectorised operations allow these processors to exploit parallelism, leading to faster execution times compared to sequential processing. -> Compact Code: Vectorised code tends to be more concise and expressive compared to equivalent code using explicit loops. -> Memory Efficiency: Vectorised operations often lead to better memory locality and cache utilisation, which can further improve performance by reducing memory access times.

Powerful state of art CPUs and GPUs on HPCs alone cannot make simulations significantly faster.   Complex simulations need innovative algorithms that can give better result with HPCs with GPUs, simulations still consume significant time. The bottleneck? Outdated algorithms. BosonQ Psi (BQP) is revolutionizing simulation by introducing cutting-edge quantum-powered algorithms. Traditional algorithms developed three to four decades ago, have become obsolete, given the complexity and tech advancements. Not only do simulations take time, but some complex simulations cannot even be performed.   At BosonQ Psi (BQP), we are redefining the standards of simulation to harness the power of modern GPUs efficiently. Our quantum-inspired optimization algorithms are reducing simulations for optimizing designs significantly. 

Run CUDA on AMD GPUs?? Maybe.... The team at Spectral Compute have developed a new GPGPU programming toolkit called SCALE that allows CUDA applications to be natively compiled and run on AMD GPUs. Basically it is just converting nvcc dialect CUDA to corresponding ROCm libraries for AMD GPUs. What's interesting is it has been tested with some projects which I use regularly 1. llama.cpp 2. FAISS 3. XGBoost (haven't been using it so much lately 🙈) There has been so much effort lately to find alternatives for NVIDIA, do you think we will get any sensible alternatives which will work in production? Let me know your thoughts... Link to the article: https://lnkd.in/dCVApmfi #CUDA #AMD #GPUs #GPUComputing #TechInnovation

The story of xBiDa with GPUs and CPUs 🦾 CPUs are great for handling tasks in sequence, perfect for general-purpose computing, while GPUs excel in parallel processing, ideal for tasks like AI and complex data processing. Our Achievement at xBiDa At xBiDa we faced a challenge: our work demanded powerful processing, and while GPUs are often the go-to for speed and complexity, we set out to optimize our algorithms for CPU. Through careful tuning, we succeeded in getting the results we needed, even within the constraints of a CPU. It was a powerful reminder that with the right approach, sometimes we can achieve big results even with limited resources. #TechInnovation #CPUVsGPU #Xbida #AlgorithmOptimization

🔍 CPUs vs. GPUs: What's the Difference? CPUs are great for handling tasks one at a time, making them ideal for general computer tasks we use daily. GPUs, however, are built to work on many tasks at once, making them perfect for complex tasks like artificial intelligence and data processing. 🎯 Why does it matter? Understanding these differences helps us choose the right tech for specific jobs, making our work faster and smarter. 💬 How are you using CPUs or GPUs in your work? #TechTips #AI #DataProcessing #Innovation #TechEssentials

Latest Swarm Updates 🔥 - Agent's can now output json, yaml, csv, dictionaries, and more: `output_type=”csv”, # “json”, “dict”, “csv” OR “string” soon “yaml”` - Execute agents on specific CPU cores, GPUs, or even GPU clusters! - You can automate the creation of the agent system prompt with `auto_generate_prompt=True` ! - SwarmRouter can now dynamically search for the best swarm for your use-case with the swarm_type="auto" feature. To get these updates run: $ pip3 install -U swarms Stay tuned for what's coming next... It's going to change the game.