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 Outdated algorithms.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.
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With AGI becoming prohibitively compute-hungry with the ever increasing number of parameters, and therefore suited for GPUs, especially for the model training task, how much longer before narrow-AI becomes suitable for CPU-based inference first and then expand to training? IEEE article from June 23 spoke of this case not being entirely dead, but a year later, where are we?
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Unleash the performance of PCIe 6.x for next-gen #AI! PCIe 6.x technology is revolutionizing how GPUs, CPUs, and AI accelerators handle demanding AI workloads in hyperscale systems. To unlock their full potential, rigorous testing is crucial for ensuring seamless plug-and-play interoperability – a must for rapid AI development. Discover how we support standards compliance and system-level interoperability of these components with our Aries Smart DSP Retimers in our #Cloud-Scale Interop Lab. Learn more about 'Why We Test'!" Watch now:
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Pipelined Data Masters is a new feature for Imagination's D-Series GPUs. It allows the firmware to set up (pipeline) the next job while a previous job is still processing within the GPU. Effectively, the firmware work overlaps with GPU work instead of running serialised in-between jobs. This approach enables higher performance for the same level of core, as we avoid idle cycles and improve utilisation of the GPU processing hardware, which means a better return on investment. Find out more in our blog: https://hubs.ly/Q02LWd3m0 #GPU #PowerVR
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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
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In Dec 2023, #AMD showed a 4x performance advantage on the #Instinct #MI300 #APU vs. traditional discrete #GPUs. Many asked, how was this accomplished? And how can other teams achieve similar acceleration? We are pleased to announce a publication at: https://lnkd.in/gy94ea-9 appearing at #ISC 2024, "Porting HPC Applications to AMD Instinct MI300A Using Unified Memory and OpenMP" which is intended to serve as a guide to application developers on how to program, using portable directives such as OpenMP, to leverage the tight integration of CPU and GPUs on the same package in the same memory space (i.e., the APU). The paper encompasses the programming model, memory model, and performance profiling in OpenFOAM's HPC_motorbike. The 4x performance benefit came from eliminating page migrations, rapid access between the CPU or GPU cores, and increased memory bandwidth delivered to the CPUs. Be sure to stop by the technical talk by Suyash Tandon, Ph.D. at ISC on Tuesday, May 14th! More details at: https://t.co/dkNIIQm0LF Brent Gorda Daniele Piccarozzi, MBA Hisaki Ohara Nicholas Malaya
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Transformative Technology Leader | Driving Digital Transformation & Innovation | Helping Businesses Create Lasting Success, Growth & Efficiency with Innovative Technology
🔍 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
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
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Better CFD Performance with Heterogeneous CPU-GPU Load Balancing 🚀The Load balancing using both CPUs and GPUs has improved the performance of a turbulent flow simulation by up to 87% compared to GPU-only execution. This was achieved by strategically distributing computationally intensive turbulent inlet regions to CPUs while assigning the less demanding bulk regions to GPUs. 🔬 The inhomogeneous spatial domain decomposition was optimized using a cutting-edge genetic algorithm tailored for cost-aware optimization. This method ensures that each simulation part is processed on the most suitable hardware, maximizing efficiency. 💻 The simulation ran on a single accelerated CPU-GPU node of the HoreKa supercomputer, utilizing OpenLB's support for MPI, OpenMP, AVX-512 vectorization, and CUDA. With 355 million lattice cells, the system achieved an impressive throughput of ~19.25 billion cell updates per second for the NSE-only case. 🔗 Learn More: OpenLB.net 🔗 Read the Preprint: https://lnkd.in/dsYVdbbZ 💳 Credits: openlb Simulation Setup: Fedor Bukreev Heterogeneous Load Balancing & Visualization: Adrian Kummerländer #HPC #CFD #OpenLB #LoadBalancing #CPU #GPU #Supercomputing #PerformanceOptimization #LatticeBoltzmann #Simulation #TechEngineering #HoreKa #HighPerformanceComputing
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👀 Great visual on the capabilites of CPU and GPU
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
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On Dec 2023, #AMD showed a 4x performance advantage on the #Instinct #MI300 #APU vs. traditional discrete #GPUs. Many asked, how was this accomplished? And how can other teams achieve similar acceleration? I'm pleased to announce a publication at: https://lnkd.in/gy94ea-9 appearing at #ISC 2024, "Porting HPC Applications to AMD Instinct MI300A Using Unified Memory and OpenMP" which is intended to serve as a guide to application developers on how to program, using portable directives such as OpenMP, to leverage the tight integration of CPU and GPUs on the same package in the same memory space (i.e., the APU). The paper encompasses the programming model, memory model, and performance profiling in OpenFOAM's HPC_motorbike. The 4x performance benefit came from eliminating page migrations, rapid access between the CPU or GPU cores, and increased memory bandwidth delivered to the CPUs. Be sure to stop by the technical talk by Suyash Tandon, Ph.D. at ISC on Tuesday, May 14th! More details at: https://t.co/dkNIIQm0LF Many thanks to co-authors: Carlo Bertolli, Leopold Grinberg, Gheorghe-Teodor Bercea, Mark Olesen, and Simone Bnà
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