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jit: Transition from linear to more effective form #238
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when the using frequency of a block exceeds a predetermined threshold, the baseline tiered1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the baseline JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tiered1 machine code generator, and code cache. Furthermore, this baseline JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. Related: sysprog21#238
when the using frequency of a block exceeds a predetermined threshold, the baseline tiered1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the baseline JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tiered1 machine code generator, and code cache. Furthermore, this baseline JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. Related: sysprog21#238
when the using frequency of a block exceeds a predetermined threshold, the baseline tiered1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the baseline JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tiered1 machine code generator, and code cache. Furthermore, this baseline JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. Related: sysprog21#238
when the using frequency of a block exceeds a predetermined threshold, the baseline tiered1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the baseline JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tiered1 machine code generator, and code cache. Furthermore, this baseline JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. Related: sysprog21#238
when the using frequency of a block exceeds a predetermined threshold, the baseline tiered1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the baseline JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tiered1 machine code generator, and code cache. Furthermore, this baseline JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. Related: sysprog21#238
when the using frequency of a block exceeds a predetermined threshold, the baseline tiered1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the baseline JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tiered1 machine code generator, and code cache. Furthermore, this baseline JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier 1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier 1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier 1 machine code generator, and code cache. Furthermore, this tier 1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier 1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier 1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier 2 compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. | Metric | rv32emu(JIT-T1) | qemu | |----------+-----------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier 1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier 1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier 1 machine code generator, and code cache. Furthermore, this tier 1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier 1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier 1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier 2 compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu(JIT-T1) | qemu | |----------+-----------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier 1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu(JIT-T1) | qemu | |----------+-----------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier 1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier 1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier 1 machine code generator, and code cache. Furthermore, this tier 1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier 1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier 1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier 2 compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier 1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier 1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier 1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier 1 machine code generator, and code cache. Furthermore, this tier 1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier 1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier 1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier 2 compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier 1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier 1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier 1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier 1 machine code generator, and code cache. Furthermore, this tier 1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier 1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier 1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier 2 compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier 1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
When the using frequency of a block exceeds a predetermined threshold, the tier-1 JIT compiler traces the chained block and generate corresponding low quailty machine code. The resulting target machine code is stored in the code cache for future utilization. The primary objective of introducing the tier-1 JIT compiler is to enhance the execution speed of RISC-V instructions. This implementation requires two additional components: a tier-1 machine code generator, and code cache. Furthermore, this tier-1 JIT compiler serves as the foundational target for future improvements. In addition, we have developed a Python script that effectively traces code templates and automatically generates JIT code templates. This approach eliminates the need for manually writing duplicated code. As shown in the performance analysis below, the tier-1 JIT compiler's performance closely parallels that of QEMU in benchmarks with a constrained dynamic instruction count. However, for benchmarks featuring a substantial dynamic instruction count or lacking specific hotspots—examples include pi and STRINGSORT—the tier-1 JIT compiler demonstrates noticeably slower execution compared to QEMU. Hence, a robust tier-2 JIT compiler is essential to generate optimized machine code across diverse execution paths, coupled with a runtime profiler for detecting hotspots. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.02| 0.031| |mandelbrot| 0.029| 0.0115| |puzzle | 0.0115| 0.009| |pi | 0.0413| 0.0177| |dhrystone | 0.331| 0.393| |Nqeueens | 0.854| 0.749| |qsort-O2 | 2.384| 2.16| |miniz-O2 | 1.33| 1.01| |primes-O2 | 2.93| 1.069| |sha512-O2 | 2.057| 0.939| |stream | 12.747| 10.36| |STRINGSORT| 89.012| 11.496| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 186,228|1,343,012| |mandelbrot| 152,203| 841,841| |puzzle | 153,423| 890,225| |pi | 152,923| 879,957| |dhrystone | 154,466| 856,404| |Nqeueens | 154,880| 858,618| |qsort-O2 | 155,091| 933,506| |miniz-O2 | 165,627|1,076,682| |primes-O2 | 150,540| 928,446| |sha512-O2 | 153,553| 978,177| |stream | 165,911| 957,845| |STRINGSORT| 167,871|1,104,702| Related: sysprog21#238
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
We follow the template and API of X64 to implement A64 tier-1 JIT compiler. * Perfromance | Metric | rv32emu-T1C | qemu | |----------+-------------+-------| |aes | 0.034| 0.045| |puzzle | 0.0115| 0.0169| |pi | 0.035| 0.032| |dhrystone | 1.914| 2.005| |Nqeueens | 3.87| 2.898| |qsort-O2 | 7.819| 11.614| |miniz-O2 | 7.604| 3.803| |primes-O2 | 10.551| 5.986| |sha512-O2 | 6.497| 2.853| |stream | 52.25| 45.776| As demonstrated in the memory usage analysis below, the tier-1 JIT compiler utilizes less memory than QEMU across all benchmarks. * Memory usage | Metric | rv32emu-T1C | qemu | |----------+-------------+---------| |aes | 183,212|1,265,962| |puzzle | 145,239| 891,357| |pi | 144,739| 872,525| |dhrystone | 146,282| 853,256| |Nqeueens | 146,696| 854,174| |qsort-O2 | 146,907| 856,721| |miniz-O2 | 157,475| 999,897| |primes-O2 | 142,356| 851,661| |sha512-O2 | 145,369| 901,136| |stream | 157,975| 955,809| Related: sysprog21#238 Close: sysprog21#296
After the merge of tier-1 JIT compiler, it is time to revisit our IR. |
Modern CPUs invest substantial effort in predicting these indirect branches, but the Branch Target Buffer (BTB) has its limitations in size. Eliminating any form of indirect call or jump, including those through dispatch tables, is greatly beneficial. This is because contemporary CPUs are equipped with large reorder buffers that can process extensive code efficiently, provided branch prediction is effective. However, in larger programs with widespread use of indirect jumps, optimal branch prediction becomes increasingly challenging. |
FEX is an advanced x86 emulation frontend, crafted to facilitate the running of x86 and x86-64 binaries on Arm64 platforms, comparable to qemu-user. At the heart of FEX's emulation capability is the FEXCore, which employs an SSA-based Intermediate Representation (IR) crafted from the input x86-64 assembly. Working with SSA is particularly advantageous during the translation of x86-64 code to IR, throughout the optimization stages with custom passes, and when transferring the IR to FEX's CPU backends. Key aspects of FEX's emulation IR include:
These features underscore FEX's design philosophy, emphasizing precise control, optimization flexibility, and efficient translation mechanisms within its emulation environment. Reference: FEXCore IR |
The Java HotSpot Server Compiler (C2) utilizes a Sea-of-Nodes IR form designed for high performance with minimal overhead, similar to LLVM's approach with its control flow graph (CFG). However, in textual IR presentations, the CFG is not depicted as a traditional 'graph' but rather through labels and jumps that effectively outline the graph's edges. Like C2’s IR, the Sea-of-Nodes IR can be described in a linear textual format and only visually represented as a "graph" when loaded into memory. This allows for flexibility in handling nodes without control dependencies, known as "floating nodes," which can be placed in any basic block in the textual format and reassigned in memory to maintain their floating characteristic. While the current tier-2 JIT compiler, built with LLVM, offers aggressive optimizations, it is also resource-intensive, consuming considerable memory and prolonging compilation times. An alternative, the IR Framework, emerges as a viable option that enhances performance while minimizing memory usage. This framework not only defines an IR but also offers a streamlined API for IR construction, coupled with algorithms for optimization, scheduling, register allocation, and code generation. The code generated in-memory can be executed directly, potentially increasing efficiency. The Ideal Graph Visualizer (IGV) is a tool designed for developers to analyze and troubleshoot performance issues by examining compilation graphs. It specifically focuses on IR graphs, which serve as a language-independent bridge between the source code and the machine code generated by compilers. |
Inspired by rvdbt, we may adopt its QuickIR, a lightweight non-SSA internal representation used by the QMC compiler. QuickIR interacts with both local and global states; the former represents optimized temporaries, while the latter includes the emulated CPU state and any internal data structures attached to CPUState, a concept common to many emulators. The terms QuickIR sample (1) - single basic block
QuickIR sample (2) - conditional branch representation
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sovietov_graph_irs_2023.pdf |
The chained block structure used by both interpreter and tier-1 compiler is linear, with each block pointing only to the subsequent block. Enhancing a block to reference its previous block brings significant value, especially for hot spot profiling. This advancement paves the way for developing a graph-based intermediate representation (IR). In this IR, graph edges symbolize use-define chains. Rather than working on a two-tiered Control-Flow Graph (CFG) comprising basic blocks (tier 1) and instructions (tier 2), analyses and transformations will directly interact with and modify this use-def information in a streamlined, single-tiered graph structure.
The sfuzz project employs a custom intermediate representation. The initial step in the actual code generation process involves lifting the entire function into this intermediate representation. During the initialization phase, when the target is first loaded, the size of the function is determined. This is achieved by parsing the elf metadata and creating a hashmap that maps function start addresses to their respective sizes.
The IR-lifting process iterates through the original instructions and generates an IR instruction for each original instruction using a large switch statement. The following example illustrates what the intermediate representation might resemble for a very minimal function that essentially performs a branch operation based on a comparison in the first block.
Reference: A Simple Graph-Based Intermediate Representation
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